Archives: Glossary Terms

How Affiliate Fraud Works

+-----------------------+      +-----------------------+      +---------------------+
|   Fraudulent Source   |----->|   Affiliate Network   |----->| Advertiser's System |
| (Bot, Click Farm, etc)|      |    (Tracking Link)    |      | (Website/App)       |
+-----------------------+      +-----------------------+      +---------------------+
            │                                                              │
            │                                                              ▼
            │                                                +-------------------------+
            └───────────────────────────────────────────────>| Traffic Analysis Engine |
                                                             +-------------------------+
                                                                         │
                  +------------------------------------------------------+------------------------------------------------------+
                  │                                                      │                                                      │
                  ▼                                                      ▼                                                      ▼
     +------------------------+                          +-------------------------------+                          +-------------------------+
     │ IP & Device Analysis   │                          │      Behavioral Heuristics    │                          │   Attribution Check     │
     │ (Proxy/Botnet Check)   │                          │ (Click Speed, Low Time-on-Page) │                          │ (Cookie Stuffing, etc.) │
     +------------------------+                          +-------------------------------+                          +-------------------------+
                  │                                                      │                                                      │
                  └───────────────────────────────────────────────┬───────────────────────────────────────────────────────┘
                                                                  ▼
                                                    +--------------------------+
                                                    |  Fraud Decision & Action |
                                                    | (Block, Flag, Challenge) |
                                                    +--------------------------+

🧠 Core Detection Logic

Example 1: Conversion Time Anomaly

This logic flags conversions that happen too quickly after the initial click. An impossibly short time between a click and a successful purchase or form submission is a strong indicator of bot activity, as real users require time to read, evaluate, and act.

FUNCTION check_conversion_time(click_event, conversion_event):
  time_diff = conversion_event.timestamp - click_event.timestamp

  // Set a minimum threshold (e.g., 3 seconds)
  MIN_TIME_THRESHOLD = 3

  IF time_diff < MIN_TIME_THRESHOLD:
    RETURN "FLAG_AS_FRAUD"
  ELSE:
    RETURN "LEGITIMATE"

Example 2: IP Address Reputation

This logic checks the incoming IP address against known blocklists of data centers, proxies, and botnets. Traffic originating from these sources is almost always non-human and fraudulent. This is a fundamental first-line defense in any traffic protection system.

FUNCTION check_ip_reputation(click_event):
  ip = click_event.ip_address

  // Load known fraudulent IP lists
  proxy_list = LOAD_LIST("proxies.txt")
  datacenter_list = LOAD_LIST("datacenters.txt")

  IF ip IN proxy_list OR ip IN datacenter_list:
    RETURN "BLOCK_TRAFFIC"
  ELSE:
    RETURN "ALLOW_TRAFFIC"

Example 3: Geographic Mismatch

This logic verifies if the location of the IP address matches the geographic information provided in billing or shipping details. A significant mismatch, such as an IP from one country and a shipping address in another, is a red flag for fraud using stolen information.

FUNCTION check_geo_mismatch(click_event, purchase_data):
  ip_location = GET_GEOLOCATION(click_event.ip_address)
  billing_country = purchase_data.billing_country

  IF ip_location.country != billing_country:
    // Increase fraud score or trigger manual review
    INCREASE_FRAUD_SCORE(click_event.session_id, 20)
    RETURN "FLAG_FOR_REVIEW"
  ELSE:
    RETURN "OK"

📈 Practical Use Cases for Businesses

• Campaign Budget Protection – By filtering out fake clicks and conversions from bots, businesses ensure their advertising budget is spent only on genuine potential customers, directly improving ROI.
• Data Integrity for Analytics – Preventing fraudulent traffic ensures that marketing analytics (like conversion rates and user engagement) are accurate. This allows businesses to make reliable, data-driven decisions about strategy and resource allocation.
• Affiliate Program Health – Automatically identifying and removing fraudulent partners protects the commissions of legitimate affiliates. This maintains a fair and attractive program, encouraging high-quality partners to join and remain active.
• Chargeback Reduction – By blocking transactions made with stolen credit cards, a common tactic in affiliate fraud, businesses can significantly reduce the number of costly chargebacks and associated penalties.

Example 1: IP Blacklisting Rule

# This rule blocks traffic from IPs known for fraudulent activity.
# It is applied at the earliest stage of traffic entry.

RULE "Block High-Risk IPs"
WHEN
  IncomingRequest.ip IN GlobalFraudIPBlacklist
THEN
  Action: Block
  Log: "Blocked known fraudulent IP: " + IncomingRequest.ip
END

Example 2: Session Conversion Velocity

# This rule flags affiliates whose traffic converts at an unnatural rate,
# which often points to automated scripts or click farms.

RULE "Flag Suspicious Conversion Speed"
WHEN
  Conversions.Count(AffiliateID, last_5_minutes) > 50 AND
  TimeBetweenClickAndConversion(AffiliateID, average) < 5_seconds
THEN
  Action: FlagAffiliate(AffiliateID)
  Log: "Affiliate " + AffiliateID + " flagged for high conversion velocity."
END

🐍 Python Code Examples

This code demonstrates a simple way to check for click fraud by identifying if multiple clicks are coming from the same IP address in a short period. This helps detect basic bot activity or manual click farms.

from collections import defaultdict
from datetime import datetime, timedelta

clicks = [
    {'ip': '8.8.8.8', 'timestamp': '2025-07-15T10:00:00Z'},
    {'ip': '1.1.1.1', 'timestamp': '2025-07-15T10:00:01Z'},
    {'ip': '8.8.8.8', 'timestamp': '2025-07-15T10:00:02Z'},
    {'ip': '8.8.8.8', 'timestamp': '2025-07-15T10:00:03Z'},
]

def detect_click_frequency(click_stream, time_window_seconds=60, max_clicks=10):
    ip_clicks = defaultdict(list)
    fraudulent_ips = set()

    for click in click_stream:
        ip = click['ip']
        timestamp = datetime.fromisoformat(click['timestamp'].replace('Z', '+00:00'))
        
        # Remove clicks outside the time window
        ip_clicks[ip] = [ts for ts in ip_clicks[ip] if timestamp - ts < timedelta(seconds=time_window_seconds)]
        
        ip_clicks[ip].append(timestamp)

        if len(ip_clicks[ip]) > max_clicks:
            fraudulent_ips.add(ip)
            
    return fraudulent_ips

fraud_ips = detect_click_frequency(clicks, max_clicks=2)
print(f"Fraudulent IPs detected: {fraud_ips}")

This example analyzes conversion data to find affiliates with unusually high chargeback rates. A high rate suggests the affiliate may be generating fake sales using stolen credit cards or other fraudulent means.

import pandas as pd

data = {
    'affiliate_id': ['aff_123', 'aff_456', 'aff_123', 'aff_123', 'aff_456'],
    'is_chargeback': [False, False, True, True, False]
}
df = pd.DataFrame(data)

def find_high_chargeback_affiliates(dataframe, threshold=0.5):
    chargeback_rates = dataframe.groupby('affiliate_id')['is_chargeback'].mean()
    suspicious_affiliates = chargeback_rates[chargeback_rates > threshold]
    return suspicious_affiliates

suspicious = find_high_chargeback_affiliates(df)
print("Affiliates with suspicious chargeback rates:")
print(suspicious)

Types of Affiliate Fraud

• Cookie Stuffing - This method involves dropping multiple affiliate tracking cookies onto a user's device without their consent. If the user later makes a purchase, the fraudster illegitimately gets credit, stealing commissions from honest affiliates or organic traffic.
• Click Fraud - Fraudsters use automated bots or human "click farms" to generate a large number of clicks on pay-per-click (PPC) affiliate links. This inflates the apparent traffic sent by the affiliate, leading to unearned payouts from the advertiser.
• Lead Fraud - This occurs when affiliates submit fake or stolen information to an advertiser's lead generation forms (e.g., for quotes or newsletters). These leads are worthless as they do not represent genuinely interested customers, but the affiliate still gets paid per lead.
• Domain Spoofing & Typosquatting - Scammers register domain names that are common misspellings of popular brands (typosquatting) or otherwise mimic legitimate sites (spoofing). They place affiliate links on these fake sites, tricking users and stealing traffic and commissions intended for the actual brand.
• Transaction Fraud - This involves using stolen credit card details to make purchases through an affiliate link. The fraudster receives a commission for the "sale," but the transaction eventually results in a chargeback for the merchant, causing financial loss and damaging payment processor relationships.

How AI Fraud Detection Works

Incoming Traffic → [Data Collection & Preprocessing] → [AI Analysis Engine] → [Risk Scoring] → [Action & Mitigation] → [Feedback Loop]
    (Clicks, Impressions) │        (User Agent, IP, etc.)     │ (ML Models, Rules)  │ (Assigns Fraud Score) │   (Block, Flag, Alert)      │   (Retrains AI Model)
                         └───────────────────────────────────┘                     └─────────────────────┘                             └───────────────────────┘

🧠 Core Detection Logic

Example 1: Behavioral Anomaly Detection

This logic analyzes patterns in user behavior to distinguish between genuine human interactions and automated bots. It establishes a baseline of normal activity and flags deviations, such as impossibly high click rates or unnatural mouse movements. This is central to traffic protection as it can identify sophisticated bots that mimic human actions.

FUNCTION analyze_behavior(session_data):
  // Define normal behavior thresholds
  max_clicks_per_minute = 15
  min_time_on_page = 2 // seconds
  max_page_scroll_speed = 3000 // pixels per second

  // Calculate metrics from session data
  click_rate = session_data.clicks / session_data.duration_minutes
  scroll_speed = session_data.pixels_scrolled / session_data.duration_seconds

  // Check for anomalies
  IF click_rate > max_clicks_per_minute THEN
    RETURN "FRAUDULENT: High click velocity"
  END IF

  IF session_data.duration_seconds < min_time_on_page THEN
    RETURN "FRAUDULENT: Insufficient dwell time"
  END IF

  RETURN "LEGITIMATE"
END FUNCTION

Example 2: IP Reputation and Geolocation Mismatch

This logic checks the reputation of an IP address against known sources of fraud, such as data center proxies or VPNs, which are often used to conceal a user's true location. It also flags inconsistencies between a user's stated location and their IP-based location, a common sign of fraud. This is a foundational element in filtering out low-quality traffic.

FUNCTION check_ip_reputation(ip_address, user_country):
  // Check against known datacenter/VPN IP lists
  is_datacenter_ip = is_in_datacenter_list(ip_address)
  
  IF is_datacenter_ip THEN
    RETURN "FRAUDULENT: Traffic from known datacenter"
  END IF

  // Check for geographic mismatch
  ip_country = get_country_from_ip(ip_address)
  
  IF ip_country != user_country THEN
    RETURN "FRAUDULENT: IP location does not match user profile"
  END IF

  RETURN "LEGITIMATE"
END FUNCTION

Example 3: Session Scoring with Multiple Heuristics

This approach combines multiple data points into a single risk score to assess the legitimacy of a session. Instead of relying on one factor, it aggregates evidence from various heuristics, such as time of day, device fingerprint, and navigation path. A higher score indicates a greater likelihood of fraud, allowing for more nuanced and accurate filtering.

FUNCTION calculate_risk_score(session_data):
  risk_score = 0

  // Heuristic 1: Click timestamp anomaly (e.g., clicks outside business hours)
  IF is_outside_normal_hours(session_data.click_time) THEN
    risk_score = risk_score + 20
  END IF

  // Heuristic 2: Suspicious user agent
  IF is_known_bot_user_agent(session_data.user_agent) THEN
    risk_score = risk_score + 50
  END IF

  // Heuristic 3: Rapid navigation path
  IF session_data.pages_visited > 5 AND session_data.duration_seconds < 10 THEN
    risk_score = risk_score + 30
  END IF

  // Final decision based on score
  IF risk_score > 60 THEN
    RETURN "HIGH_RISK_FRAUD"
  ELSE IF risk_score > 30 THEN
    RETURN "MEDIUM_RISK_FLAG_FOR_REVIEW"
  ELSE
    RETURN "LOW_RISK"
  END IF
END FUNCTION

📈 Practical Use Cases for Businesses

• Campaign Shielding – AI automatically detects and blocks invalid clicks from bots and competitors, preventing the exhaustion of PPC budgets and ensuring ads are shown to genuine potential customers.
• Marketing Analytics Integrity – By filtering out fraudulent traffic, AI ensures that marketing analytics (e.g., click-through rates, conversion rates) reflect real user engagement, leading to more accurate data-driven decisions.
• Return on Ad Spend (ROAS) Improvement – AI fraud detection improves ROAS by stopping wasted ad spend on fraudulent interactions. This reallocates the budget toward channels and audiences that deliver actual conversions and value.
• Lead Quality Enhancement – For businesses focused on lead generation, AI helps ensure that form fills and sign-ups come from legitimate users, not bots, thus improving the quality of leads passed to sales teams.

Example 1: Geofencing Rule

// USE CASE: A local service business wants to ensure its ads are only clicked by users within its service area.
// LOGIC: Block clicks from IP addresses that resolve to locations outside a predefined geographic radius.

FUNCTION apply_geofence(user_ip, campaign_settings):
  allowed_radius_km = 50
  business_location = campaign_settings.target_location
  
  user_location = get_location_from_ip(user_ip)
  distance = calculate_distance(user_location, business_location)

  IF distance > allowed_radius_km THEN
    // Action: Block the click and add IP to a temporary blocklist
    block_ip(user_ip)
    log_event("Blocked click from outside geofence", user_ip)
    RETURN FALSE
  END IF

  RETURN TRUE
END FUNCTION

Example 2: Session Score Threshold

// USE CASE: An e-commerce site wants to prevent bots from adding items to carts and skewing inventory data.
// LOGIC: Score each session based on multiple behavioral factors. If the score exceeds a certain threshold, block the user before they can interact with the site.

FUNCTION check_session_score(session):
  score = 0
  
  // Rule 1: High frequency of clicks in short time
  IF session.click_count > 10 AND session.time_on_site_seconds < 5 THEN
    score = score + 40
  END IF

  // Rule 2: Use of a known proxy or VPN service
  IF is_proxy_ip(session.ip_address) THEN
    score = score + 50
  END IF

  // Rule 3: No mouse movement detected
  IF session.mouse_events == 0 THEN
    score = score + 10
  END IF

  // Block if score is dangerously high
  IF score > 75 THEN
    block_user(session.user_id)
    log_event("Session blocked due to high fraud score", session.user_id, score)
    RETURN FALSE
  END IF

  RETURN TRUE
END FUNCTION

🐍 Python Code Examples

This code defines a function to analyze click timestamps from a specific IP address. It helps identify click fraud by detecting an unnaturally high frequency of clicks within a short time window, a common indicator of bot activity.

def is_rapid_fire_click(click_logs, ip_address, time_window_seconds=10, max_clicks=5):
    """Checks if an IP has an unusually high number of clicks in a given time window."""
    from datetime import datetime, timedelta

    recent_clicks = 0
    now = datetime.now()
    time_threshold = now - timedelta(seconds=time_window_seconds)

    # Filter clicks from the specific IP within the time window
    ip_clicks = [
        log for log in click_logs 
        if log['ip'] == ip_address and log['timestamp'] > time_threshold
    ]

    if len(ip_clicks) > max_clicks:
        print(f"Fraud Detected: IP {ip_address} made {len(ip_clicks)} clicks in the last {time_window_seconds} seconds.")
        return True
    
    return False

# Example Usage:
click_data = [
    {'ip': '192.168.1.1', 'timestamp': datetime.now() - timedelta(seconds=1)},
    {'ip': '192.168.1.1', 'timestamp': datetime.now() - timedelta(seconds=2)},
    {'ip': '192.168.1.1', 'timestamp': datetime.now() - timedelta(seconds=3)},
    {'ip': '10.0.0.5', 'timestamp': datetime.now() - timedelta(seconds=4)},
    {'ip': '192.168.1.1', 'timestamp': datetime.now() - timedelta(seconds=5)},
    {'ip': '192.168.1.1', 'timestamp': datetime.now() - timedelta(seconds=6)},
    {'ip': '192.168.1.1', 'timestamp': datetime.now() - timedelta(seconds=7)},
]
is_rapid_fire_click(click_data, '192.168.1.1')

This script filters incoming traffic by checking the user agent string against a blocklist of known bots and crawlers. This is a simple yet effective method to block basic automated traffic before it can generate fraudulent clicks on ads.

def filter_suspicious_user_agents(traffic_request, bot_signatures):
    """Blocks requests from user agents matching a list of known bot signatures."""
    user_agent = traffic_request.get('user_agent', '').lower()

    for signature in bot_signatures:
        if signature in user_agent:
            print(f"Fraud Detected: Blocking request from known bot: {user_agent}")
            return False  # Block the request
            
    return True # Allow the request

# Example Usage:
bot_user_agents = ['bot', 'crawler', 'spider', 'headlesschrome']
legitimate_request = {'user_agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36...'}
fraudulent_request = {'user_agent': 'AhrefsBot/7.0; +http://ahrefs.com/robot/'}

filter_suspicious_user_agents(legitimate_request, bot_user_agents)
filter_suspicious_user_agents(fraudulent_request, bot_user_agents)

This function calculates a basic authenticity score for a user session based on multiple behavioral heuristics. By combining factors like time spent on page, clicks, and scrolls, it provides a more nuanced assessment of whether the traffic is from a real user or a bot.

def score_traffic_authenticity(session_metrics):
    """Calculates a simple score to gauge the authenticity of a user session."""
    score = 0
    
    # More time on page is a good sign
    if session_metrics['dwell_time_seconds'] > 10:
        score += 30
    
    # Some interaction is good, but too much is suspicious
    if 1 < session_metrics['clicks'] < 15:
        score += 25
    
    # Scrolling suggests human interaction
    if session_metrics['scroll_depth_percent'] > 20:
        score += 20
        
    # No interaction is a red flag
    if session_metrics['clicks'] == 0 and session_metrics['scroll_depth_percent'] == 0:
        score -= 50

    print(f"Session authenticity score: {score}")
    return score

# Example Usage:
human_like_session = {'dwell_time_seconds': 45, 'clicks': 3, 'scroll_depth_percent': 60}
bot_like_session = {'dwell_time_seconds': 2, 'clicks': 100, 'scroll_depth_percent': 0}

score_traffic_authenticity(human_like_session)
score_traffic_authenticity(bot_like_session)

Types of AI Fraud Detection

• Supervised Learning – This method uses labeled historical data to train models. The AI learns from past examples of fraudulent and legitimate clicks to identify known types of fraud with high accuracy. It is effective for recognizing established patterns of malicious behavior.
• Unsupervised Learning – This approach is used to find previously unknown types of fraud by identifying anomalies or outliers in the data. Without relying on predefined labels, it can detect new and evolving fraudulent tactics by spotting behavior that deviates from the norm, making it critical for proactive defense.
• Deep Learning – A subset of machine learning, deep learning uses neural networks with many layers (like CNNs and RNNs) to analyze vast and complex datasets. It excels at identifying subtle, intricate patterns in user behavior, click sequences, and session data that simpler models might miss, which is ideal for detecting sophisticated bots.
• Reinforcement Learning – This type of AI learns by taking actions and receiving rewards or penalties. In fraud detection, it can be used to dynamically adjust blocking rules in real-time. The system learns which actions are most effective at stopping fraud while minimizing the blocking of legitimate users.

How AIPowered Analytics Works

Incoming Traffic (Click/Impression)
           │
           ▼
+---------------------+
│   Data Collection   │
│ (IP, UA, Timestamp) │
+---------------------+
           │
           ▼
+---------------------+
│     AI Analysis     │
│ (Pattern, Behavior) │
+---------------------+
           │
           ▼
+---------------------+
│   Scoring & Risk    │
│    Assessment       │
+---------------------+
           │
     ┌─────┴─────┐
     ▼           ▼
+----------+  +-----------+
│ Legimate │  │ Fraudulent│
│ Traffic  │  │ Traffic   │
+----------+  +-----------+
     │           └─→ +----------+
     │               │ Blocking │
     └───────────→   │ Action   │
                     +----------+

🧠 Core Detection Logic

Example 1: Session Velocity Scoring

This logic analyzes the frequency and timing of events within a single user session. It helps catch automated bots that perform actions much faster than a typical human user. It’s a fundamental check in real-time traffic filtering.

FUNCTION analyze_session_velocity(session_events):
  // Set a minimum time between clicks (e.g., 2 seconds)
  MIN_CLICK_INTERVAL = 2.0 

  // Check time difference between consecutive events
  timestamps = session_events.get_timestamps()
  FOR i FROM 1 TO length(timestamps):
    time_diff = timestamps[i] - timestamps[i-1]
    IF time_diff < MIN_CLICK_INTERVAL:
      RETURN "FRAUDULENT: Click velocity too high"
  
  RETURN "LEGITIMATE"

Example 2: Geographic Mismatch Detection

This logic compares the IP address's geographic location with other location-based signals, such as user-provided data or browser timezone settings. A significant mismatch can indicate the use of a proxy or VPN to mask the user's true location, a common tactic in ad fraud.

FUNCTION check_geo_mismatch(ip_location, browser_timezone):
  // Get expected timezone from IP location
  expected_timezone = lookup_timezone(ip_location)

  // Compare with browser's reported timezone
  IF expected_timezone != browser_timezone:
    RETURN "SUSPICIOUS: Geo-IP does not match browser timezone"

  RETURN "LEGITIMATE"

Example 3: Bot-Like User-Agent Filtering

This logic inspects the User-Agent (UA) string sent by the browser. Bots often use outdated, generic, or known non-standard UA strings. This check acts as a first line of defense to filter out low-sophistication bots.

FUNCTION filter_user_agent(user_agent_string):
  // Maintain a list of known bot or suspicious UA signatures
  BOT_SIGNATURES = ["headless-chrome", "phantomjs", "dataprovider", "curl"]

  // Check if the UA string contains any bot signatures
  FOR signature IN BOT_SIGNATURES:
    IF signature IN user_agent_string.lower():
      RETURN "FRAUDULENT: Known bot User-Agent"

  RETURN "LEGITIMATE"

📈 Practical Use Cases for Businesses

• Campaign Shielding: Actively block fraudulent clicks from PPC campaigns to prevent budget exhaustion. This ensures that ad spend is directed toward genuine potential customers, maximizing return on investment.
• Analytics Purification: Filter out bot and fraudulent traffic from analytics platforms. This provides a clear and accurate view of real user engagement, leading to better-informed marketing strategy decisions.
• Lead Generation Integrity: Prevent fake form submissions and sign-ups on lead generation forms. This ensures the sales pipeline is filled with qualified leads, improving sales team efficiency and conversion rates.
• Return on Ad Spend (ROAS) Optimization: By eliminating wasteful spending on fraudulent interactions, AIPowered Analytics directly improves ROAS. Advertisers can reallocate saved funds to high-performing channels, enhancing overall campaign profitability.

Example 1: Geofencing Rule

This pseudocode demonstrates a geofencing rule that blocks traffic from locations outside the business's target market, a common strategy to reduce exposure to click farms concentrated in specific regions.

PROCEDURE apply_geofence(click_data):
  ALLOWED_COUNTRIES = ["US", "CA", "GB"]
  
  ip_address = click_data.get("ip")
  country = get_country_from_ip(ip_address)

  IF country NOT IN ALLOWED_COUNTRIES:
    block_traffic(ip_address)
    log_event("Blocked IP due to geofence rule", ip_address)
  ELSE:
    allow_traffic(ip_address)

Example 2: Session Authenticity Scoring

This pseudocode shows a simplified scoring model that combines multiple checks to assess the authenticity of a session. A cumulative score determines if the traffic is legitimate, suspicious, or fraudulent.

FUNCTION score_session_authenticity(session):
  score = 0
  
  // Check for data center IP
  IF is_datacenter_ip(session.ip):
    score += 40
  
  // Check for headless browser signature
  IF has_headless_browser_signature(session.user_agent):
    score += 30
    
  // Check for rapid clicks
  IF session.click_frequency > 5 per minute:
    score += 30

  IF score >= 70:
    RETURN "FRAUDULENT"
  ELSE IF score >= 40:
    RETURN "SUSPICIOUS"
  ELSE:
    RETURN "LEGITIMATE"

🐍 Python Code Examples

This Python function simulates the detection of abnormally high click frequency from a single IP address within a short time frame, a strong indicator of bot activity.

# A simple dictionary to store click timestamps for each IP
click_log = {}
from collections import deque
import time

def is_click_frequency_abnormal(ip_address, time_window=60, max_clicks=10):
    """Checks if an IP has an unusually high click frequency."""
    current_time = time.time()
    
    if ip_address not in click_log:
        click_log[ip_address] = deque()
    
    # Remove timestamps older than the time window
    while (click_log[ip_address] and 
           click_log[ip_address] < current_time - time_window):
        click_log[ip_address].popleft()
        
    click_log[ip_address].append(current_time)
    
    if len(click_log[ip_address]) > max_clicks:
        print(f"ALERT: High frequency detected for IP {ip_address}")
        return True
        
    return False

# Simulation
is_click_frequency_abnormal("192.168.1.100") # Returns False
for _ in range(15):
    is_click_frequency_abnormal("192.168.1.101") # Will return True after 10 clicks

This code snippet provides a basic filter to identify and block requests originating from known data centers or using suspicious user agents, common sources of non-human traffic.

def filter_suspicious_sources(ip_address, user_agent):
    """Filters traffic from known bot-like user agents and data center IPs."""
    # Simplified list of suspicious User-Agent keywords
    SUSPICIOUS_UA_KEYWORDS = ['bot', 'crawler', 'spider', 'headless']
    
    # Simplified list of known data center IP ranges (for example purposes)
    DATACENTER_IP_PREFIXES = ['104.16.0.0', '35.180.0.0']

    # Check User-Agent
    for keyword in SUSPICIOUS_UA_KEYWORDS:
        if keyword in user_agent.lower():
            return "Blocked: Suspicious User-Agent"
    
    # Check IP prefix
    for prefix in DATACENTER_IP_PREFIXES:
        if ip_address.startswith(prefix):
            return "Blocked: Data Center IP"
            
    return "Allowed: Traffic appears legitimate"

# Simulation
print(filter_suspicious_sources("35.180.12.34", "Mozilla/5.0..."))
print(filter_suspicious_sources("8.8.8.8", "MyAwesomeBrowser/1.0 (Headless)"))
print(filter_suspicious_sources("92.154.10.1", "Mozilla/5.0..."))

Types of AIPowered Analytics

• Predictive Analytics: This type uses historical data and machine learning algorithms to forecast potential fraudulent activities. By identifying risk factors and patterns associated with past fraud, it can predict which traffic sources or user segments are likely to be fraudulent in the future, allowing for preemptive blocking.
• Behavioral Analytics: This approach focuses on analyzing user behavior patterns in real-time, such as mouse movements, session duration, and click-through rates. It distinguishes between natural human interactions and the rigid, automated patterns of bots, flagging behavior that deviates from the established norm for legitimate users.
• Anomaly Detection: Anomaly detection identifies rare events or observations that are significantly different from the majority of the data. In traffic protection, it flags sudden spikes in clicks from a specific IP, unusual geographic activity, or other patterns that don't conform to typical campaign traffic, indicating a potential automated attack.
• Network-Level Analysis: This method examines data at the network level, such as IP reputation, ISP information, and whether the connection originates from a data center or a residential address. It helps identify fraud by recognizing if traffic is coming from sources that are unlikely to be genuine customers, such as proxy servers or known botnets.

How Anomaly Detection Works

Incoming Traffic → [Data Collection] → [Baseline Model] → [Real-time Analysis] → [Anomaly?] ┬─ Yes → [Block/Alert]
       │                  │                  │                    │                   └─ No  → [Allow]
       └──────────────────┴──────────────────┴────────────────────┘

🧠 Core Detection Logic

Example 1: Click Velocity and Frequency Capping

This logic prevents a single source from generating an unnatural number of clicks in a short period. It monitors the rate of clicks from individual IP addresses or device fingerprints and flags or blocks them if they exceed a plausible human-generated frequency, a common sign of bot activity.

// Define thresholds
max_clicks_per_minute = 5
max_clicks_per_hour = 30

FUNCTION check_click_velocity(ip_address):
  // Retrieve click history for the given IP
  clicks_last_minute = get_clicks(ip_address, last_minute)
  clicks_last_hour = get_clicks(ip_address, last_hour)

  IF count(clicks_last_minute) > max_clicks_per_minute:
    RETURN "ANOMALY: High frequency per minute"
  ELSE IF count(clicks_last_hour) > max_clicks_per_hour:
    RETURN "ANOMALY: High frequency per hour"
  ELSE:
    RETURN "NORMAL"

Example 2: Geographic Mismatch Detection

This rule identifies fraud by comparing the geographical location of a user’s IP address with other data points, such as account country or language settings. A significant mismatch, like a click from a different continent than the user’s profile, suggests the use of a proxy or VPN to mask the true origin.

FUNCTION check_geo_mismatch(click_data):
  ip_location = get_ip_geolocation(click_data.ip)
  account_country = click_data.user.country
  browser_language = click_data.headers.language

  IF ip_location.country != account_country:
    // High-confidence anomaly
    RETURN "ANOMALY: IP location mismatches account country"
  ELSE IF not is_language_common_in(browser_language, ip_location.country):
    // Lower-confidence anomaly, could be an expat
    RETURN "WARNING: Browser language is uncommon for IP location"
  ELSE:
    RETURN "NORMAL"

Example 3: Behavioral Heuristics Scoring

This logic analyzes a user’s on-site behavior to determine if it appears human. It scores sessions based on factors like mouse movement, time spent on the page, and interaction with page elements. Sessions with no mouse movement or unnaturally short durations receive a high fraud score.

FUNCTION score_session_behavior(session_data):
  fraud_score = 0

  IF session_data.time_on_page < 2 seconds:
    fraud_score += 40
  IF session_data.mouse_events == 0:
    fraud_score += 30
  IF session_data.scrolled_page == false:
    fraud_score += 20
  IF session_data.is_from_datacenter_ip:
    fraud_score += 50 // High weight for known non-human sources

  IF fraud_score > 60:
    RETURN "ANOMALY: High probability of bot activity"
  ELSE:
    RETURN "NORMAL"

📈 Practical Use Cases for Businesses

• Campaign Shielding – Automatically block clicks from known fraudulent sources, such as data centers and botnets, to prevent ad budget waste before it occurs and protect campaign performance.
• Data Integrity Assurance – Filter out non-human and invalid traffic to ensure that analytics dashboards and marketing reports reflect genuine user engagement, leading to more accurate business decisions.
• Conversion Funnel Protection – Prevent fake leads and automated form submissions by analyzing user behavior patterns, ensuring that the sales team engages with legitimate prospects and not bots.
• Return on Ad Spend (ROAS) Optimization – Improve ROAS by eliminating spend on fraudulent clicks that will never convert. This reallocates the budget toward channels and audiences that deliver real, valuable customers.

Example 1: Geofencing for Local Campaigns

A local business running a geo-targeted campaign can use anomaly detection to enforce strict geofencing. This logic ensures that only users from the intended geographic areas can trigger ad clicks, instantly blocking traffic from outside the target region.

// Rule: Only allow clicks from the specified target state (e.g., California)
FUNCTION enforce_geofence(click_ip, target_state):
  user_location = get_geolocation(click_ip)

  IF user_location.state == target_state:
    RETURN "ALLOW"
  ELSE:
    log_fraud_attempt(click_ip, "Geo-mismatch")
    RETURN "BLOCK"

Example 2: Session Authenticity Scoring

An e-commerce site can score traffic authenticity to protect against various threats. This logic combines multiple checks, such as verifying if the browser is real and checking for a history of fraudulent activity associated with the device fingerprint, to generate a trust score.

FUNCTION calculate_trust_score(session):
  score = 100 // Start with a perfect score

  IF is_headless_browser(session.user_agent):
    score -= 50
  IF is_datacenter_ip(session.ip):
    score -= 40
  IF has_fraud_history(session.device_fingerprint):
    score -= 60

  // Anomaly if score is below a certain threshold
  IF score < 50:
    RETURN "ANOMALY_DETECTED"
  ELSE:
    RETURN "SESSION_VERIFIED"

🐍 Python Code Examples

This Python code demonstrates a basic click frequency analysis. It tracks clicks from each IP address within a specific time window and flags any IP that exceeds a defined threshold, a common indicator of automated bot activity.

from collections import defaultdict
import time

click_log = defaultdict(list)
TIME_WINDOW_SECONDS = 60
CLICK_THRESHOLD = 10

def record_click(ip_address):
    """Records a click timestamp for a given IP."""
    current_time = time.time()
    click_log[ip_address].append(current_time)
    print(f"Click recorded for {ip_address}")

def is_fraudulent(ip_address):
    """Checks if click frequency from an IP is anomalous."""
    current_time = time.time()
    # Filter out clicks older than the time window
    recent_clicks = [t for t in click_log[ip_address] if current_time - t <= TIME_WINDOW_SECONDS]
    click_log[ip_address] = recent_clicks
    
    if len(recent_clicks) > CLICK_THRESHOLD:
        print(f"ANOMALY: {ip_address} has {len(recent_clicks)} clicks in the last minute.")
        return True
    return False

# Simulation
record_click("192.168.1.100")
# Rapid clicks from a fraudulent source
for _ in range(12):
    record_click("198.51.100.2")

is_fraudulent("192.168.1.100") # Returns False
is_fraudulent("198.51.100.2")  # Returns True

This example provides a function to filter traffic based on suspicious user agents. It checks if a session's user agent string matches known patterns associated with bots or automated scripts, helping to block non-human traffic at the entry point.

# List of known suspicious user agent substrings
BOT_USER_AGENTS = [
    "bot",
    "spider",
    "crawler",
    "headless", # Common in automated browser scripts
    "python-requests"
]

def filter_by_user_agent(user_agent):
    """Filters traffic based on the user agent string."""
    ua_string_lower = user_agent.lower()
    for bot_ua in BOT_USER_AGENTS:
        if bot_ua in ua_string_lower:
            print(f"ANOMALY: Suspicious user agent detected: {user_agent}")
            return False # Block this traffic
    
    print(f"User agent is valid: {user_agent}")
    return True # Allow this traffic

# Simulation
filter_by_user_agent("Mozilla/5.0 (Windows NT 10.0; Win64; x64) ...") # Returns True
filter_by_user_agent("Googlebot/2.1 (+http://www.google.com/bot.html)") # Returns False
filter_by_user_agent("python-requests/2.25.1") # Returns False

Types of Anomaly Detection

• Supervised Detection – This method uses a labeled dataset containing examples of both normal and fraudulent traffic to train a model. It is highly accurate at identifying known types of fraud but is less effective against new, unseen attack patterns, as it requires prior data on the threat.
• Unsupervised Detection – This type of detection does not require labeled data. Instead, it identifies anomalies by assuming that most traffic is normal and flagging any data points that deviate significantly from the established baseline. It excels at finding novel or emerging threats that have no predefined signature.
• Semi-Supervised Detection – This hybrid approach uses a model trained exclusively on normal traffic data. Any event that does not conform to the model of normal behavior is flagged as an anomaly. It is useful when fraudulent data is scarce or unavailable for training.
• Statistical Anomaly Detection – This technique applies statistical models to identify outliers. It calculates metrics like mean, standard deviation, and distribution to define a normal range and flags any data point that falls outside this range as anomalous. It is effective for detecting clear deviations in numerical data.
• Clustering-Based Detection – This method groups similar data points into clusters. Data points that do not belong to any cluster or are far from the nearest cluster's center are considered anomalies. This is effective for identifying coordinated fraudulent activity originating from related sources.

How Anomaly Detection Algorithms Works

Incoming Traffic (Clicks, Impressions)
           │
           ▼
+----------------------+
│ Data Collection      │
│ (IP, UA, Timestamp)  │
+----------------------+
           │
           ▼
+----------------------+
│ Feature Extraction   │
│ (Session, Behavior)  │
+----------------------+
           │
           ▼
+----------------------+
│ Anomaly Detection    │
│ (Compares to Normal) │
+----------------------+
           │
           ├─ Legitimate Traffic ─→ Delivered to Site
           │
           └─ Anomalous Traffic ──→ Blocked/Flagged

🧠 Core Detection Logic

Example 1: High-Frequency Click Analysis

This logic identifies and flags IP addresses that generate an abnormally high number of clicks in a short time frame. It’s a common technique to catch bots or click farm participants who are paid to repeatedly click on ads. This fits into traffic protection by setting a threshold for normal behavior and blocking sources that exceed it.

// Define click frequency thresholds
MAX_CLICKS_PER_MINUTE = 5
MAX_CLICKS_PER_HOUR = 30

// Function to check click frequency for an IP
function checkClickFrequency(ipAddress, clickTimestamp) {
  // Get historical click data for the IP
  let clicks_last_minute = getClicksFrom(ipAddress, last_60_seconds);
  let clicks_last_hour = getClicksFrom(ipAddress, last_3600_seconds);

  if (clicks_last_minute > MAX_CLICKS_PER_MINUTE) {
    return "ANOMALOUS_HIGH_FREQUENCY";
  }

  if (clicks_last_hour > MAX_CLICKS_PER_HOUR) {
    return "ANOMALOUS_HIGH_FREQUENCY";
  }

  return "NORMAL";
}

Example 2: Session Duration Heuristics

This logic analyzes the time a user spends on a landing page after clicking an ad. Sessions that are unnaturally short (e.g., less than one second) are often indicative of bots that click a link and immediately leave. This heuristic helps filter out low-quality or non-human traffic by measuring engagement.

// Define minimum session duration
MINIMUM_DWELL_TIME_SECONDS = 1.0

// Function to evaluate session validity
function validateSession(session) {
  // Calculate time between landing and leaving the page
  let dwellTime = session.exitTimestamp - session.entryTimestamp;

  if (dwellTime < MINIMUM_DWELL_TIME_SECONDS) {
    // Flag the click associated with this session as fraudulent
    flagClickAsFraud(session.clickId, "UNNATURALLY_SHORT_SESSION");
    return "INVALID";
  }

  return "VALID";
}

Example 3: Geo-Mismatch Detection

This logic checks for inconsistencies between a user's stated location and their IP address's geolocation. For instance, if a user's browser language is set to Russian but their IP is from a data center in Vietnam, it could signal the use of a proxy or VPN to mask their true origin, a common tactic in ad fraud.

// Function to check for geographic consistency
function checkGeoMismatch(clickData) {
  let ipLocation = getGeoFromIP(clickData.ipAddress); // e.g., 'Vietnam'
  let browserLanguage = clickData.browserLanguage; // e.g., 'ru-RU'
  let timezone = clickData.timezone; // e.g., 'America/New_York'

  // If IP country does not align with language or timezone, flag it
  if (isMismatch(ipLocation, browserLanguage, timezone)) {
    return "SUSPICIOUS_GEO_MISMATCH";
  }

  return "CONSISTENT";
}

📈 Practical Use Cases for Businesses

• Campaign Shielding – Automatically block traffic from known bots, data centers, and suspicious IP addresses, ensuring that ad budgets are spent on reaching real, potential customers rather than on fraudulent clicks.
• Analytics Purification – By filtering out invalid traffic before it hits the analytics platform, businesses can maintain clean data. This allows for accurate measurement of key performance indicators (KPIs) and a true understanding of campaign effectiveness.
• Return on Ad Spend (ROAS) Improvement – Preventing budget waste on fraudulent interactions directly improves ROAS. More of the ad spend reaches genuine users, leading to a higher likelihood of conversions and a better return on investment.
• Lead Quality Enhancement – By ensuring that website traffic comes from legitimate sources, anomaly detection helps improve the quality of sales leads. This saves sales teams from wasting time on fake or low-intent form submissions generated by bots.

Example 1: Data Center IP Blocking Rule

This pseudocode demonstrates a rule to block traffic originating from known data centers, as this is a common source of non-human bot traffic. Businesses use this to prevent bots from interacting with their ads and skewing performance data.

// Function to process an incoming ad click
function processClick(click) {
  let ip = click.ipAddress;

  // Check if the IP address belongs to a known data center
  if (isDataCenterIP(ip)) {
    // Block the click and add IP to a temporary blocklist
    blockIP(ip);
    logEvent("Blocked data center IP: " + ip);
    return "BLOCKED";
  }

  // If not a data center IP, allow it
  return "ALLOWED";
}

Example 2: Session Authenticity Scoring

This logic assigns a trust score to a user session based on multiple behavioral factors. A very low score indicates bot-like behavior. Businesses use this to dynamically filter out suspicious users who might pass simpler checks but fail behavioral analysis.

// Function to score a user session
function scoreSession(session) {
  let score = 100;

  // Deduct points for suspicious behavior
  if (session.timeOnPage < 2) {
    score -= 40; // Very short visit
  }
  if (session.mouseMovements == 0) {
    score -= 30; // No mouse activity
  }
  if (session.scrollDepth < 10) {
    score -= 20; // Did not scroll down the page
  }

  // If score is below a threshold, flag as bot
  if (score < 50) {
    flagAsBot(session.user);
    return "FRAUDULENT";
  }

  return "LEGITIMATE";
}

🐍 Python Code Examples

This Python function simulates the detection of click fraud by identifying any IP address that generates more than a set number of clicks within a given time window. It helps in blocking IPs that exhibit bot-like rapid-clicking behavior.

import time

# Store click timestamps for each IP
click_data = {}
# Set fraud detection limits
CLICK_LIMIT = 10
TIME_WINDOW_SECONDS = 60

def is_fraudulent_click(ip_address):
    current_time = time.time()
    if ip_address not in click_data:
        click_data[ip_address] = []

    # Remove clicks outside the time window
    click_data[ip_address] = [t for t in click_data[ip_address] if current_time - t < TIME_WINDOW_SECONDS]

    # Add the new click
    click_data[ip_address].append(current_time)

    # Check if click count exceeds the limit
    if len(click_data[ip_address]) > CLICK_LIMIT:
        print(f"Fraudulent activity detected from IP: {ip_address}")
        return True
    return False

# Simulate clicks
is_fraudulent_click("192.168.1.10") # Returns False
# Simulate a bot clicking 15 times
for _ in range(15):
    is_fraudulent_click("8.8.8.8") # Will return True after the 10th click

This script filters incoming traffic by examining the User-Agent string. It blocks requests from common bot or script user agents, which is a straightforward way to filter out a significant portion of non-human traffic.

# List of known suspicious User-Agent substrings
BOT_USER_AGENTS = [
    "python-requests",
    "curl",
    "wget",
    "Scrapy",
    "headless-chrome"
]

def filter_by_user_agent(user_agent_string):
    # Check if any bot signature is in the user agent
    for bot_signature in BOT_USER_AGENTS:
        if bot_signature.lower() in user_agent_string.lower():
            print(f"Blocked suspicious user agent: {user_agent_string}")
            return False # Block the request
    return True # Allow the request

# Example usage
filter_by_user_agent("Mozilla/5.0 (Windows NT 10.0; Win64; x64) ...") # Returns True
filter_by_user_agent("python-requests/2.25.1") # Returns False

Types of Anomaly Detection Algorithms

• Rule-Based Detection – This type uses predefined rules and thresholds to identify fraud. For example, a rule might block any IP address that clicks on an ad more than 10 times in a minute. It is simple to implement but can be easily bypassed by sophisticated bots.
• Statistical Anomaly Detection – This method applies statistical models to identify data points that are outliers from the norm. For instance, it analyzes the distribution of clicks over time and flags periods with abnormal spikes in activity. This approach is effective at finding unusual patterns in large datasets.
• Machine Learning-Based Detection – This approach uses algorithms trained on historical data to recognize complex patterns of both fraudulent and legitimate behavior. Unsupervised learning can identify new types of fraud without labeled data, making it highly adaptable to evolving threats from bots and click farms.
• Behavioral Analysis – This type focuses on user behavior, such as mouse movements, typing speed, and page navigation patterns. It creates a profile of typical human interaction and flags sessions that lack these organic behaviors, which is effective for identifying advanced bots designed to mimic human clicks.

How API Security Works

Incoming Click/Impression Request
           │
           ▼
+----------------------+
│   API Gateway/WAF    │
│ (Initial Filtering)  │
+----------------------+
           │
           ▼
+-------------------------+
│ API Security Middleware │
│   (Deep Analysis)       │
└──────────┬──────────────┘
           │
           ├─→ [Authentication & Authorization]
           │
           ├─→ [Rate Limiting & Throttling]
           │
           ├─→ [Behavioral & Heuristic Analysis]
           │
           └─→ [Signature & Pattern Matching]
                      │
                      ▼
+-------------------------+
│     Decision Engine     │
│   (Allow / Block)     │
+-------------------------+
           │
     ┌─────┴─────┐
     ▼           ▼
 [Legitimate]  [Fraudulent]
 To Ad Server  Blocked/Logged

🧠 Core Detection Logic

Example 1: Behavioral Heuristics

This logic analyzes the sequence and timing of user actions to detect non-human behavior. It’s effective against simple bots that execute actions faster than a human possibly could. This check fits within the deep analysis phase of a traffic protection system.

FUNCTION check_behavioral_heuristics(session_data):
  // Check time between page load and ad click
  time_to_click = session_data.click_timestamp - session_data.load_timestamp
  IF time_to_click < 1.0 THEN
    RETURN {fraud: true, reason: "Click too fast"}
  END IF

  // Check mouse movement before click
  IF session_data.mouse_movements < 5 THEN
    RETURN {fraud: true, reason: "Insufficient mouse activity"}
  END IF

  // Check for impossibly straight mouse path
  IF is_path_linear(session_data.mouse_path) THEN
      RETURN {fraud: true, reason: "Linear mouse path detected"}
  END IF

  RETURN {fraud: false}
END FUNCTION

Example 2: Session Anomaly Detection

This logic tracks the consistency of signals within a single user session. It helps identify sophisticated bots that try to mask their identity by rotating IPs or user agents, a common technique in distributed fraud attacks. This logic is applied after initial data collection.

FUNCTION check_session_anomaly(session_events):
  // Get unique IP addresses and user agents from the session
  unique_ips = get_unique_values(session_events, "ip_address")
  unique_user_agents = get_unique_values(session_events, "user_agent")

  // Flag session if multiple IPs or UAs are used
  IF count(unique_ips) > 2 THEN
    RETURN {fraud: true, reason: "Multiple IPs in single session"}
  END IF

  IF count(unique_user_agents) > 1 THEN
    RETURN {fraud: true, reason: "User agent changed mid-session"}
  END IF

  RETURN {fraud: false}
END FUNCTION

Example 3: Geo Mismatch Detection

This logic compares the IP address geolocation with other location signals, like timezone settings from the browser or GPS data from a mobile app. It's highly effective at catching traffic originating from data centers or VPNs attempting to mimic users from high-value regions.

FUNCTION check_geo_mismatch(request_data):
  ip_location = get_geo_from_ip(request_data.ip_address) // e.g., 'USA'
  browser_timezone = request_data.headers['Browser-Timezone'] // e.g., 'Asia/Kolkata'
  language = request_data.headers['Accept-Language'] // e.g., 'ru-RU'

  // If IP country does not align with browser timezone or language
  IF ip_location == 'USA' AND contains(browser_timezone, 'Asia') THEN
    RETURN {fraud: true, reason: "IP location and timezone mismatch"}
  END IF

  IF ip_location == 'USA' AND language == 'ru-RU' THEN
      RETURN {fraud: true, reason: "IP location and language mismatch"}
  END IF

  RETURN {fraud: false}
END FUNCTION

📈 Practical Use Cases for Businesses

• Campaign Shielding – API security rules can automatically block traffic from data centers and known VPN providers, ensuring that ad spend is directed toward real consumers in the targeted geographic areas.
• Analytics Purification – By filtering out bot clicks and fake impressions at the API level, businesses ensure their analytics dashboards reflect genuine user engagement, leading to more accurate performance metrics and better strategic decisions.
• Budget Protection – Implementing rate limiting and throttling on click APIs prevents rapid, automated clicks from a single source, directly protecting campaign budgets from being exhausted by fraudulent activity.
• Lead Generation Integrity – For campaigns focused on lead generation, API security can validate form submissions in real time, rejecting entries from disposable email addresses or those showing bot-like typing patterns to keep the sales pipeline clean.

Example 1: Geofencing Rule

This pseudocode demonstrates a common business rule used to protect campaigns that target specific countries. The API rejects any click request originating from an IP address outside the allowed geographic region, saving budget and improving targeting accuracy.

FUNCTION enforce_geofencing(request):
  ALLOWED_COUNTRIES = ["US", "CA", "GB"]
  
  ip_address = request.ip
  country_code = get_country_from_ip(ip_address)

  IF country_code NOT IN ALLOWED_COUNTRIES:
    // Block the request and log the event
    log_event("Blocked click from non-targeted country: " + country_code)
    REJECT_REQUEST()
  ELSE:
    // Allow the request to proceed
    PROCESS_REQUEST()
  END IF
END FUNCTION

Example 2: Session Scoring Logic

This logic provides a more nuanced approach than a simple block/allow rule. It assigns risk scores to different suspicious behaviors observed during a user's session. A click is only blocked if the cumulative score exceeds a predefined threshold, reducing the risk of flagging legitimate users (false positives).

FUNCTION calculate_fraud_score(session):
  score = 0
  
  IF session.uses_vpn():
    score += 40
  
  IF session.is_headless_browser():
    score += 50

  IF session.time_on_page < 2_seconds:
    score += 20
    
  IF session.clicks > 5 in 1_minute:
    score += 30

  RETURN score
END FUNCTION

//-- Main click processing --//
fraud_score = calculate_fraud_score(current_session)
IF fraud_score >= 80:
  REJECT_CLICK("High fraud score: " + fraud_score)
ELSE:
  ACCEPT_CLICK()
END IF

🐍 Python Code Examples

This example demonstrates how to filter incoming ad click requests based on a predefined list of suspicious IP addresses. This is a fundamental technique for blocking known bad actors or traffic from non-target regions.

# List of known fraudulent IP addresses
BLACKLISTED_IPS = {"192.168.1.101", "203.0.113.54", "198.51.100.2"}

def filter_by_ip(request_ip):
    """
    Checks if an incoming request IP is on the blacklist.
    """
    if request_ip in BLACKLISTED_IPS:
        print(f"Blocking fraudulent click from blacklisted IP: {request_ip}")
        return False
    else:
        print(f"Allowing legitimate click from IP: {request_ip}")
        return True

# Simulate incoming clicks
clicks = [{"ip": "91.200.12.4"}, {"ip": "203.0.113.54"}, {"ip": "198.18.0.1"}]
for click in clicks:
    filter_by_ip(click["ip"])

This code snippet simulates detecting click fraud by analyzing the frequency of clicks from a single user ID. Blocking users who click too frequently in a short period helps mitigate automated bot attacks designed to drain ad budgets.

from collections import defaultdict
import time

# Store click timestamps for each user
user_clicks = defaultdict(list)
CLICK_LIMIT = 5  # Max clicks
TIME_WINDOW = 60  # Within 60 seconds

def is_click_fraudulent(user_id):
    """
    Detects fraud based on high click frequency.
    """
    current_time = time.time()
    
    # Remove old clicks that are outside the time window
    user_clicks[user_id] = [t for t in user_clicks[user_id] if current_time - t < TIME_WINDOW]
    
    # Add the new click
    user_clicks[user_id].append(current_time)
    
    # Check if click count exceeds the limit
    if len(user_clicks[user_id]) > CLICK_LIMIT:
        print(f"Fraudulent activity detected for user: {user_id}")
        return True
    
    print(f"User {user_id} click is within limits.")
    return False

# Simulate clicks from a user
for _ in range(6):
    is_click_fraudulent("user-123")
    time.sleep(5)

Types of API Security

• Authentication and Authorization: This type focuses on verifying identity. Authentication confirms that users are who they say they are (e.g., with an API key), while authorization determines what they are allowed to do. It is a primary defense against unauthorized data access and fraudulent activity.
• Rate Limiting and Throttling: This method controls how often an API can be called. By setting a limit on the number of requests from a single IP address or user within a specific timeframe, it effectively mitigates bot attacks, brute-force attempts, and other forms of resource abuse designed to generate fake clicks.
• Input Validation and Schema Enforcement: This type ensures that data sent to the API conforms to expected formats, types, and values. By rejecting malformed requests, it prevents various attacks like SQL injection or parameter tampering, where attackers try to manipulate the API's logic to register fraudulent conversions or clicks.
• Behavioral Analysis: This approach uses machine learning to establish a baseline of normal user behavior and then flags deviations. In ad tech, it identifies non-human patterns like impossibly fast click speeds, repetitive navigation paths, or a lack of mouse movement, which are strong indicators of bot-driven click fraud.
• IP and Geo-Filtering: A straightforward but effective method that involves blocking or allowing API requests based on their geographic origin or IP reputation. In fraud prevention, this is used to block traffic from data centers, known proxy services, or countries outside a campaign’s target area, filtering out significant sources of invalid traffic.

How App install fraud Works

+---------------------+      +----------------------+      +---------------------+
|   Ad Campaign       |----->|   Fraudulent Actor   |----->| Attribution System  |
| (CPI Model)         |      | (Bot, Malware, Farm) |      | (MMP)               |
+---------------------+      +----------------------+      +---------------------+
          |                             │                            │
          |                             └─────────┐                  |
          ▼                                       ▼                  ▼
+---------------------+      +----------------------+      +---------------------+
| Advertiser's Budget |<-+---|  Fake Install Signal |      |  Install Validation |
| (Financial Loss)    |  |   | (Click, Download)    |----->| (IP, Device, Time)  |
+---------------------+  |   +----------------------+      +---------------------+
                         |                                          |
                         └──────────────────────────────────────────┘
                                      (Credit for Fake Install)

🧠 Core Detection Logic

Example 1: Click-to-Install Time (CTIT) Analysis

This logic identifies fraud by analyzing the time between a click and the subsequent app install. Installs that occur too quickly after a click are flagged as suspicious, as they are often generated by bots or click injection malware. This rule helps filter out automated, non-human installation patterns.

FUNCTION check_ctit(click_timestamp, install_timestamp):
  ctit_duration = install_timestamp - click_timestamp
  
  // Flag as fraud if install happens within seconds of a click
  IF ctit_duration < 10 SECONDS THEN
    RETURN "FRAUD"
  
  // Flag as suspicious if CTIT is abnormally long (click spamming)
  ELSE IF ctit_duration > 24 HOURS THEN
    RETURN "SUSPICIOUS"
  
  ELSE
    RETURN "LEGITIMATE"
  END IF
END FUNCTION

Example 2: Device and IP Anomaly Detection

This logic detects fraud by identifying patterns of multiple installs coming from a single device or IP address in a short period. It helps uncover device farms or botnets where one entity spoofs many devices to generate mass installs. This is a fundamental check in traffic protection systems.

FUNCTION analyze_device_ip(install_data_batch):
  ip_install_counts = {}
  device_id_counts = {}

  FOR install in install_data_batch:
    // Count installs per IP
    ip_install_counts[install.ip] += 1
    
    // Count installs per Device ID
    device_id_counts[install.device_id] += 1
  ENDFOR

  FOR ip, count in ip_install_counts:
    IF count > 5 THEN
      FLAG_AS_FRAUD(ip) // Block IP associated with device farm
  ENDFOR
  
  FOR device_id, count in device_id_counts:
    IF count > 3 THEN
      FLAG_AS_FRAUD(device_id) // Block device ID resetting
  ENDFOR
END FUNCTION

Example 3: Post-Install Engagement Scoring

This logic evaluates the legitimacy of an install by monitoring user behavior immediately after the app is opened. Fake installs often show zero engagement—no registrations, no in-app events, and immediate uninstalls. A low engagement score indicates the install was likely fraudulent and not from a genuine user.

FUNCTION score_install_engagement(install_id):
  // Retrieve post-install events for a given install
  events = get_events_for_install(install_id, within_first_24_hours)
  
  engagement_score = 0
  
  IF events.contains("registration_complete") THEN
    engagement_score += 50
  
  IF events.count("level_achieved") > 0 THEN
    engagement_score += 30
    
  IF events.count("session_start") < 2 THEN
    engagement_score -= 40
  
  IF engagement_score < 20 THEN
    RETURN "HIGH_FRAUD_RISK"
  ELSE
    RETURN "LOW_FRAUD_RISK"
  END IF
END FUNCTION

📈 Practical Use Cases for Businesses

• Campaign Shielding – Businesses use app install fraud detection to proactively block fake installs from fraudulent publishers, protecting their user acquisition budgets from being wasted on non-existent users and ensuring campaign funds are spent on legitimate sources.
• Data Integrity – By filtering out fraudulent install data, companies ensure their analytics are clean and reliable. This leads to more accurate key performance indicators (KPIs), better strategic decisions, and a true understanding of campaign performance.
• Return on Ad Spend (ROAS) Optimization – Preventing install fraud ensures that advertising spend is directed toward channels that deliver real, engaged users. This directly improves ROAS by eliminating attribution to fraudulent sources that provide no value.
• User Quality Assessment – Businesses analyze fraud patterns to distinguish between high-quality and low-quality traffic sources. This allows them to reallocate budgets to partners who deliver genuinely interested users, not just installs.

Example 1: Geolocation Mismatch Rule

// This logic flags installs where the IP address location
// does not match the device's claimed location, a common sign of proxy usage.

FUNCTION check_geo_mismatch(ip_location, device_location):
  IF ip_location.country != device_location.country THEN
    // High probability of fraud, flag for review
    RETURN "FRAUD_FLAG_GEO_MISMATCH"
  
  ELSE IF ip_location.city != device_location.city THEN
    // Potentially suspicious, warrants lower-level flag
    RETURN "SUSPICIOUS_GEO_DEVIATION"
    
  ELSE
    RETURN "GEO_MATCH_OK"
  END IF
END FUNCTION

Example 2: New Device Ratio Monitoring

// This logic monitors the percentage of "new" device IDs from a traffic source.
// An abnormally high ratio suggests Device ID Reset Fraud.

FUNCTION monitor_new_device_ratio(publisher_id, time_window):
  installs = get_installs(publisher_id, time_window)
  
  new_device_count = 0
  total_installs = installs.count()
  
  FOR install in installs:
    IF is_new_device(install.device_id) THEN
      new_device_count += 1
    END IF
  ENDFOR
  
  new_device_ratio = new_device_count / total_installs
  
  IF new_device_ratio > 0.85 THEN
    // Over 85% new devices is highly indicative of fraud
    BLOCK_PUBLISHER(publisher_id)
    RETURN "FRAUD_DETECTED_DEVICE_RESET"
  ELSE
    RETURN "RATIO_NORMAL"
  END IF
END FUNCTION

🐍 Python Code Examples

This Python function checks for click spamming by calculating the time between a click and an install. If the time is unrealistically short (e.g., under 10 seconds), it flags the install as potentially fraudulent, as this is a common indicator of automated click injection.

from datetime import datetime, timedelta

def check_click_to_install_time(click_time_str, install_time_str):
    """
    Analyzes the time between a click and an install to detect fraud.
    """
    click_time = datetime.fromisoformat(click_time_str)
    install_time = datetime.fromisoformat(install_time_str)
    
    time_difference = install_time - click_time
    
    if time_difference < timedelta(seconds=10):
        return "Fraudulent: Install time is too short after click."
    elif time_difference > timedelta(days=1):
        return "Suspicious: Long delay suggests click spamming."
    else:
        return "Legitimate"

# Example Usage
click = "2025-07-17T10:00:00"
install = "2025-07-17T10:00:05"
print(check_click_to_install_time(click, install))

This code simulates the detection of device farm activity by counting how many installs originate from the same IP address within a specific timeframe. Exceeding a certain threshold can indicate a fraudulent operation where multiple devices are used from one location.

from collections import defaultdict

def detect_ip_concentration(install_logs, threshold=5):
    """
    Identifies IPs with an abnormally high number of installs.
    """
    ip_counts = defaultdict(int)
    fraudulent_ips = []
    
    for log in install_logs:
        ip = log['ip_address']
        ip_counts[ip] += 1
        
    for ip, count in ip_counts.items():
        if count > threshold:
            fraudulent_ips.append(ip)
            
    if fraudulent_ips:
        return f"Fraud Detected: High install concentration from IPs {fraudulent_ips}"
    else:
        return "No IP concentration detected."

# Example Usage
logs = [
    {'ip_address': '203.0.113.10'}, {'ip_address': '203.0.113.10'},
    {'ip_address': '198.51.100.5'}, {'ip_address': '203.0.113.10'},
    {'ip_address': '203.0.113.10'}, {'ip_address': '203.0.113.10'},
    {'ip_address': '203.0.113.10'}
]
print(detect_ip_concentration(logs))

Types of App install fraud

• Click Spamming - This method involves fraudsters sending a high volume of fake ad clicks from users who have not actually made them. The goal is to be the last click recorded before an organic install occurs, thereby stealing attribution and the associated payout from the advertiser.
• SDK Spoofing - A sophisticated form of fraud where criminals fake communication signals between an app's software development kit (SDK) and attribution providers. This tricks the system into recording fake installs without any real device or app installation ever taking place, making it appear legitimate.
• Device Farms - This is a physical operation where large numbers of real mobile devices are used to manually or automatically install apps. While real devices are used, the user intent is fraudulent, as the sole purpose is to generate paid installs without any genuine engagement.
• Click Injection - In this technique, a malicious app on a user's device detects when another app is being downloaded. It then triggers a fake click just before the installation completes, hijacking the attribution for what would have been an organic install.
• Incentivized Installs - While sometimes legitimate, this method becomes fraudulent when users are offered rewards to install and immediately uninstall an app. These installs provide no value to the advertiser as the user has no genuine interest in the app and is only motivated by the incentive.

How App links Works

  User Clicks URL (e.g., ad)
           │
           ▼
+-------------------------+
│   Android OS Intercept  │
+-------------------------+
           │
           ▼
┌─────────────────────────┐
│  Check for Verified App │
│    for the URL's Domain │
└─────────────────────────┘
           │
           ├─ YES (App Link Verified) → Open App Directly to Content
           │
           └─ NO (Verification Fails) → Open URL in Web Browser

🧠 Core Detection Logic

Example 1: Digital Asset Link Verification

This is the fundamental logic for App Links. Before attributing an install or in-app event to a click, the system verifies that the click came through a verified App Link. It checks that the Android OS confirmed the association between the domain of the clicked URL and the app’s package name by successfully fetching the assetlinks.json file. Traffic from unverified links can be flagged as potentially fraudulent.

FUNCTION HandleIncomingClick(click_data):
  // Check if the click was opened via a verified App Link
  is_verified_app_link = AndroidOS.isVerified(click_data.source_url)

  IF is_verified_app_link == TRUE:
    // Trust the source, app ownership is confirmed
    ProcessAttribution(click_data)
    Log("Traffic from verified source.")
  ELSE:
    // Link is a standard deep link or web URL, not a secure App Link
    FlagAsPotentiallyHijacked(click_data)
    Log("WARNING: Traffic source not verified. Potential for hijacking.")
  END IF
END FUNCTION

Example 2: Mismatch Detection between Click and Install

This logic cross-references the domain that initiated the click with the app that was installed or opened. In a legitimate App Link flow, the source domain of the ad click must match the domain declared and verified in the installed app’s manifest. A mismatch indicates that the click was likely redirected or that a different app intercepted the intent.

FUNCTION ValidateInstallSource(click_domain, installed_app_package):
  // Get the verified domains associated with the installed app package
  verified_domains = getVerifiedDomainsForPackage(installed_app_package)

  IF click_domain IN verified_domains:
    // The click source is a verified owner of the app
    RETURN "Install is Legitimate"
  ELSE:
    // The click came from a domain not associated with the app
    RETURN "Install is Suspicious: Domain Mismatch"
  END IF
END FUNCTION

Example 3: Blocking Non-HTTPS Traffic

App Link verification requires the `assetlinks.json` file to be hosted on an HTTPS server. Ad traffic protection systems can enforce this by automatically flagging or discarding any purported “App Link” clicks that do not use the HTTPS scheme. This prevents downgrade attacks where an attacker might try to intercept traffic on an insecure connection.

FUNCTION FilterByScheme(click_event):
  url_scheme = getScheme(click_event.url)

  // Android App Links must use HTTP/HTTPS URLs, with verification over HTTPS
  IF url_scheme != "https":
    Log("FRAUD_ATTEMPT: Non-HTTPS scheme used for App Link. Blocking.")
    RETURN FALSE
  END IF

  RETURN TRUE
END FUNCTION

📈 Practical Use Cases for Businesses

• Campaign Integrity: Ensures that paid traffic from ad campaigns is directed exclusively to the official app, preventing competitors or fraudsters from stealing installs through malicious redirects.
• Secure User Onboarding: Protects users who click on ads or promotional links by guaranteeing they land within the legitimate app environment, safeguarding them from phishing or data theft attempts.
• Accurate Attribution: Provides a reliable signal for attribution platforms. Since App Links are verified, it confirms that the click and the resulting install or event belong to the genuine brand owner, leading to cleaner data.
• Preventing Organic Poaching: Stops fraudulent apps from intercepting organic traffic intended for the official app, ensuring that users searching for a brand are not diverted to a malicious third party.

Example 1: Ad Campaign Traffic Filtering Rule

This pseudocode defines a rule within a traffic filtering system. It only allows clicks to be attributed to a campaign if the click was routed through a verified App Link, ensuring ad spend is not wasted on hijacked traffic.

RULE ad_traffic_filter:
  WHEN incoming_click.source_type == "AdCampaign"
  IF NOT incoming_click.is_verified_app_link:
    ACTION:
      block_attribution(incoming_click.id)
      log_event("Blocked unverified click from Campaign " + incoming_click.campaign_id)
  ELSE:
    ACTION:
      approve_attribution(incoming_click.id)
END RULE

Example 2: Geofencing with App Link Verification

This logic combines geographic checks with App Link verification. It flags traffic as highly suspicious if it originates from an unexpected region and also fails the App Link verification, a common pattern in bot-driven fraud.

FUNCTION process_mobile_event(event):
  is_verified = event.is_app_link_verified
  is_geo_valid = check_geolocation(event.ip, event.campaign_target_countries)

  // Suspicious if from a bad location AND not a verified app link
  IF is_verified == FALSE AND is_geo_valid == FALSE:
    SCORE event.fraud_score += 50
    REASON.add("Unverified app source from invalid geography")
  ELSE IF is_verified == FALSE:
    SCORE event.fraud_score += 10
    REASON.add("Unverified app source")
  END IF
END FUNCTION

🐍 Python Code Examples

This example simulates checking a hosted `assetlinks.json` file to verify if an app’s package name and certificate fingerprint are authorized by a domain owner. This is a crucial step in preventing a malicious app from associating itself with a legitimate website.

import json
import hashlib

# Mock assetlinks.json content hosted on a server
ASSET_LINKS_CONTENT = """
[{
  "relation": ["delegate_permission/common.handle_all_urls"],
  "target": {
    "namespace": "android_app",
    "package_name": "com.example.officialapp",
    "sha256_cert_fingerprints":
    ["14:6D:E9:83:C5:73:06:50:D8:EE:B9:95:2F:34:FC:64:16:A0:83:42:E6:1D:BE:A8:8A:04:96:B2:3F:CF:44:E5"]
  }
}]
"""

def verify_app_link_association(domain, package_name, certificate_fingerprint):
    """
    Simulates fetching and verifying the assetlinks.json file.
    """
    # In a real scenario, this would be a network request to https://{domain}/.well-known/assetlinks.json
    try:
        hosted_data = json.loads(ASSET_LINKS_CONTENT)
        for association in hosted_data:
            target = association.get("target", {})
            if (target.get("namespace") == "android_app" and
                target.get("package_name") == package_name and
                certificate_fingerprint in target.get("sha256_cert_fingerprints", [])):
                print(f"VERIFIED: {package_name} is a trusted handler for {domain}.")
                return True
    except json.JSONDecodeError:
        print("ERROR: Invalid JSON in assetlinks file.")
        return False

    print(f"FAILED: {package_name} is NOT a trusted handler for {domain}.")
    return False

# --- Simulation ---
# Legitimate app trying to prove ownership
verify_app_link_association(
    "example.com",
    "com.example.officialapp",
    "14:6D:E9:83:C5:73:06:50:D8:EE:B9:95:2F:34:FC:64:16:A0:83:42:E6:1D:BE:A8:8A:04:96:B2:3F:CF:44:E5"
)
# Fraudulent app trying to associate with the same domain
verify_app_link_association(
    "example.com",
    "com.malicious.fakeapp",
    "AA:BB:CC:DD:..."
)

This code simulates a server-side check to flag clicks that are not coming from verified App Links. Traffic protection systems use such logic to score incoming clicks, where clicks failing this verification are marked as suspicious because they are vulnerable to interception.

class ClickEvent:
    def __init__(self, url, was_os_verified):
        self.url = url
        self.was_os_verified = was_os_verified # This boolean is set by the Android OS
        self.fraud_score = 0
        self.reasons = []

def analyze_click_authenticity(click_event):
    """
    Analyzes a click to determine if it's from a verified source.
    """
    if not click_event.was_os_verified:
        click_event.fraud_score += 25
        click_event.reasons.append("Click did not originate from a verified App Link.")
        print(f"SUSPICIOUS: Click for {click_event.url} is not verified.")
    else:
        print(f"OK: Click for {click_event.url} is verified.")
    return click_event

# --- Simulation ---
# A click that the OS confirmed was from a verified App Link
legit_click = ClickEvent("https://example.com/product/123", was_os_verified=True)
analyze_click_authenticity(legit_click)

# A click from a regular URL or unverified deep link
suspicious_click = ClickEvent("http://some-other-site.com/product/123", was_os_verified=False)
analyze_click_authenticity(suspicious_click)

Types of App links

• Verified App Links: These are standard HTTP/S links that have been cryptographically verified to belong to a specific app. The verification happens when the Android OS successfully validates the `assetlinks.json` file on the web domain, making this the most secure type for preventing click fraud.
• Standard Deep Links: These use a custom URL scheme (e.g., `myapp://content`) to open an app. They are not automatically verified and can be claimed by multiple apps, creating a security risk where malicious apps can intercept clicks intended for a legitimate app.
• Web Links: These are regular HTTP/S URLs that have not been associated with an app via the App Link verification process. When clicked, they typically open in a web browser, but a dialog may appear if multiple apps claim they can handle the link, creating ambiguity.
• Deferred Deep Links: This is a marketing technology, not a link type itself. It allows a user who clicks a link but doesn’t have the app installed to be first taken to the app store, and then, after installation, be redirected to the specific in-app content from the original click.

How App stickiness Works

User Action (Click) → [Traffic Security Gateway] → Session Tracker Initiated
                                │
                                │
                                └─ Session Data Collection
                                   (IP, User-Agent, Timestamps, Events)
                                              │
                                              │
                    +-------------------------+-------------------------+
                    │                         │                         │
            [Behavioral Analysis]     [Heuristic Rules]       [Signature Matching]
   (Time on page, scroll, clicks)   (Click frequency, geo-mismatch)  (Known bot patterns)
                    │                         │                         │
                    └───────────+─────────────┘                         │
                                │                                       │
                         [Stickiness Score Calculation]                 │
                                │                                       │
                                │                                       │
                      +─────────┴─────────+
                      │                   │
               [Score > Threshold?]  [Block/Flag]
                      │
                      │
              [Allow Traffic]

🧠 Core Detection Logic

Example 1: Session Frequency Analysis

This logic identifies non-human velocity by tracking how many times a unique user (or a device fingerprint) clicks on ads within a short timeframe. It’s a fundamental part of a traffic protection system designed to catch basic bots and click farms that rely on high-volume, repetitive actions to generate fraudulent revenue.

SESSION_ID = get_session_id(user_ip, user_agent)
CLICK_TIMESTAMPS = get_clicks_for_session(SESSION_ID)

// Define thresholds
MAX_CLICKS_PER_MINUTE = 5
MAX_CLICKS_PER_HOUR = 30

// Analyze frequency
clicks_in_last_minute = count_clicks_within_window(CLICK_TIMESTAMPS, 60)
clicks_in_last_hour = count_clicks_within_window(CLICK_TIMESTAMPS, 3600)

// Apply rule
IF (clicks_in_last_minute > MAX_CLICKS_PER_MINUTE) OR (clicks_in_last_hour > MAX_CLICKS_PER_HOUR) THEN
  FLAG_AS_FRAUD(SESSION_ID, "High Click Frequency")
ELSE
  MARK_AS_VALID(SESSION_ID)
END IF

Example 2: Behavioral Heuristics (Time-on-Site)

This rule filters out low-quality or fraudulent traffic by measuring the user’s “dwell time” on the landing page. Clicks from bots often result in immediate bounces (zero or near-zero time on site) because their goal is just to register the click, not to engage with the content. This is a key behavioral signal for stickiness.

EVENT = get_user_event()

IF (EVENT.type == "ad_click") THEN
  SESSION_START_TIME = get_current_time()
  track_session(EVENT.session_id, SESSION_START_TIME)
END IF

IF (EVENT.type == "page_unload" OR EVENT.type == "browser_close") THEN
  SESSION = get_session_info(EVENT.session_id)
  DWELL_TIME = get_current_time() - SESSION.start_time

  // A legitimate user is expected to spend at least 2 seconds
  MINIMUM_DWELL_TIME = 2 // in seconds

  IF (DWELL_TIME < MINIMUM_DWELL_TIME) THEN
    FLAG_AS_FRAUD(EVENT.session_id, "Insufficient Dwell Time")
  END IF
END IF

Example 3: Geo-Mismatch Detection

This logic identifies sophisticated fraud where a bot's IP address location (often masked by a proxy or VPN) doesn't align with other regional indicators, like the device's language or timezone settings. This inconsistency is a strong indicator of an attempt to mimic traffic from a high-value geographic area.

// Collect session data
IP_ADDRESS = get_user_ip()
DEVICE_LANGUAGE = get_http_header("Accept-Language")
DEVICE_TIMEZONE = get_js_timezone()

// Resolve geo-data from IP
IP_GEO_LOCATION = get_geo_from_ip(IP_ADDRESS) // e.g., "USA"

// Expected language for the location
EXPECTED_LANGUAGE = get_primary_language_for_country(IP_GEO_LOCATION) // e.g., "en-US"

// Heuristic check
// A user from a US IP should typically have an English language setting
IF (IP_GEO_LOCATION == "USA" AND NOT DEVICE_LANGUAGE.starts_with("en")) THEN
  FLAG_AS_FRAUD(IP_ADDRESS, "Geo-Language Mismatch")
END IF

// Check if timezone makes sense for the IP's country
EXPECTED_TIMEZONES = get_timezones_for_country(IP_GEO_LOCATION)
IF (DEVICE_TIMEZONE NOT IN EXPECTED_TIMEZONES) THEN
  FLAG_AS_FRAUD(IP_ADDRESS, "Geo-Timezone Mismatch")
END IF

📈 Practical Use Cases for Businesses

• Campaign Shielding – App stickiness logic automatically blocks traffic from known bots and data centers, ensuring that ad budgets are spent on reaching real, potential customers, not on fraudulent clicks.
• Lead Quality Filtration – By analyzing post-click behavior, businesses can filter out fake lead form submissions originating from bots. This cleans marketing automation funnels and saves sales teams from wasting time on invalid leads.
• Analytics Accuracy – It purges fraudulent sessions from traffic reports. This provides businesses with a clearer and more accurate understanding of true user engagement, conversion rates, and campaign return on investment (ROI).
• Retargeting Optimization – By ensuring that only genuinely interested users are added to retargeting lists, App stickiness helps businesses run more efficient and cost-effective campaigns, improving the likelihood of conversion.

Example 1: Landing Page Engagement Rule

// This pseudocode scores a session based on user interactions.
// A low score indicates a non-engaged, likely fraudulent visitor.

FUNCTION calculate_engagement_score(session):
  score = 0
  
  // Award points for meaningful actions
  IF session.time_on_page > 5 THEN score += 10
  IF session.scrolled_percentage > 30 THEN score += 15
  IF session.mouse_moved_distance > 500 THEN score += 5
  IF session.clicked_internal_link THEN score += 20
  
  RETURN score
END FUNCTION

// Main logic
SESSION_DATA = get_session_data(user_id)
ENGAGEMENT_SCORE = calculate_engagement_score(SESSION_DATA)

IF ENGAGEMENT_SCORE < 10 THEN
  // User is likely a bot, block or flag them
  block_ip(SESSION_DATA.ip_address)
  exclude_from_analytics(user_id)
END IF

Example 2: Geofencing Validation Rule

// This logic checks if a click originates from a campaign's target geography.
// It helps prevent budget waste from clicks outside the desired market.

FUNCTION is_geo_valid(click_data, campaign_settings):
  user_country = get_country_from_ip(click_data.ip_address)
  
  IF user_country IN campaign_settings.targeted_countries THEN
    RETURN TRUE
  ELSE
    RETURN FALSE
  END IF
END FUNCTION

// Main logic
CLICK = get_latest_click()
CAMPAIGN = get_campaign_details(CLICK.campaign_id)

IF NOT is_geo_valid(CLICK, CAMPAIGN) THEN
  // Click is from an untargeted location, likely fraud or waste
  add_ip_to_blocklist(CLICK.ip_address)
  report_invalid_click(CLICK.id)
END IF

Example 3: Session Anomaly Detection Rule

// This rule flags sessions with characteristics that deviate significantly from typical user behavior.

FUNCTION check_session_anomalies(session):
  // Anomaly 1: User agent is from a known data center (e.g., AWS, Google Cloud)
  IF is_datacenter_user_agent(session.user_agent) THEN
    RETURN "Data Center Traffic"
  END IF

  // Anomaly 2: Very rapid succession of page views within the same session
  IF (session.page_view_count > 10 AND session.duration_seconds < 20) THEN
    RETURN "Rapid Fire Page Views"
  END IF
  
  // Anomaly 3: Presence of automation framework signatures
  IF contains_automation_signature(session.js_fingerprint) THEN
    RETURN "Automation Tool Detected"
  END IF
  
  RETURN "No Anomalies Found"
END FUNCTION

// Main logic
CURRENT_SESSION = get_session_info()
ANOMALY_RESULT = check_session_anomalies(CURRENT_SESSION)

IF ANOMALY_RESULT != "No Anomalies Found" THEN
  flag_session_as_suspicious(CURRENT_SESSION.id, ANOMALY_RESULT)
END IF

🐍 Python Code Examples

This code filters a list of click events, identifying and removing those that come from known fraudulent IP addresses on a blacklist. This is a primary line of defense in protecting ad campaigns from repeat offenders and recognized bot networks.

# List of known fraudulent IP addresses
FRAUDULENT_IPS = {"192.168.1.101", "203.0.113.54", "198.51.100.2"}

clicks = [
  {"ip": "8.8.8.8", "timestamp": "2024-10-26T10:00:00Z"},
  {"ip": "203.0.113.54", "timestamp": "2024-10-26T10:01:00Z"},
  {"ip": "9.9.9.9", "timestamp": "2024-10-26T10:02:00Z"},
  {"ip": "198.51.100.2", "timestamp": "2024-10-26T10:03:00Z"},
]

def filter_fraudulent_clicks(clicks, blacklist):
  clean_clicks = []
  for click in clicks:
    if click["ip"] not in blacklist:
      clean_clicks.append(click)
    else:
      print(f"Blocked fraudulent click from IP: {click['ip']}")
  return clean_clicks

valid_clicks = filter_fraudulent_clicks(clicks, FRAUDULENT_IPS)
print("Valid Clicks:", valid_clicks)

This script analyzes session data to detect users with an abnormally high click frequency within a short time window. By flagging such behavior, it helps identify automated bots that are programmed to execute rapid, repeated clicks in a non-human pattern.

from collections import defaultdict
from datetime import datetime, timedelta

session_clicks = [
  {"session_id": "abc-123", "timestamp": "2024-10-26T14:30:00Z"},
  {"session_id": "abc-123", "timestamp": "2024-10-26T14:30:02Z"},
  {"session_id": "def-456", "timestamp": "2024-10-26T14:31:00Z"},
  {"session_id": "abc-123", "timestamp": "2024-10-26T14:30:03Z"}, # 3rd click in 3 seconds
]

def detect_click_velocity(clicks, time_window_seconds=5, max_clicks=2):
  session_map = defaultdict(list)
  fraudulent_sessions = set()

  for click in clicks:
    session_id = click["session_id"]
    timestamp = datetime.fromisoformat(click["timestamp"].replace('Z', '+00:00'))
    
    session_map[session_id].append(timestamp)
    
    # Check clicks within the time window
    time_window_start = timestamp - timedelta(seconds=time_window_seconds)
    recent_clicks = [t for t in session_map[session_id] if t > time_window_start]
    
    if len(recent_clicks) > max_clicks:
      fraudulent_sessions.add(session_id)
      
  return fraudulent_sessions

flagged_sessions = detect_click_velocity(session_clicks)
if flagged_sessions:
    print(f"Detected high-frequency fraud in sessions: {flagged_sessions}")

Types of App stickiness

• Session-Based Stickiness – This is the most common form, where analysis is confined to a single user session. It evaluates the coherence of actions, from the initial click to page exit, to determine if the behavior appears human-like or automated within that continuous interaction.
• Cross-Session Stickiness – This method tracks a user's behavior across multiple visits over time. It helps identify legitimate, loyal users who return and engage regularly, distinguishing them from fraudulent actors who may use different IPs or devices for each attack and show no consistent return pattern.
• Behavioral Stickiness – This type focuses on the quality of in-session interactions, such as mouse movements, scroll depth, and form engagement. A high degree of behavioral stickiness indicates a user is genuinely interacting with the content, while erratic or non-existent actions suggest bot activity.
• Temporal Stickiness – This evaluates the timing and rhythm of user actions. It flags sessions with inhuman speed, such as clicking through multiple pages in milliseconds, or sessions with unnaturally long durations but no activity, which can be a sign of a parked bot.
• Contextual Stickiness – This variation assesses whether a user's actions are logical within the context of the app or website. For example, a user who jumps directly to a checkout page without browsing products would be flagged as contextually non-sticky and potentially fraudulent.

How App store analytics Works

[Ad Click] → +---------------------+ → [Analytics Engine] → +-----------------+ → [Action]
              | Data Collection     |                      | Rule/Model Check|
              | (IP, UA, Device ID) |                      | (Blocklist,     |
              └---------------------┘                      |  Anomaly)       |
                                                           └-----------------┘
                                                                   │
                                                                   ├─ Allow (Legitimate)
                                                                   └─ Block (Fraudulent)

🧠 Core Detection Logic

Example 1: IP Address Analysis

This logic filters traffic based on the reputation and characteristics of the incoming IP address. It is a first line of defense, blocking clicks from sources known for fraudulent activity, such as data centers or proxies, which are rarely used by genuine mobile users for app installs.

FUNCTION analyze_ip(click_ip):
  IF is_datacenter_ip(click_ip) THEN
    REJECT(click, "Data Center IP")
  ELSE IF is_on_blocklist(click_ip) THEN
    REJECT(click, "Known Fraudulent IP")
  ELSE
    ACCEPT(click)
  END IF
END FUNCTION

Example 2: Click Timestamp Anomaly (Click Flooding)

This logic identifies click flooding, a method where fraudsters send numerous clicks to claim credit for an organic install. It works by analyzing the time-to-install (TTI). An unnaturally short TTI (e.g., seconds after a click) or a very long TTI can indicate fraud, as genuine installs follow a more predictable time pattern.

FUNCTION check_tti(click_time, install_time):
  time_difference = install_time - click_time
  IF time_difference < 15 SECONDS THEN
    FLAG_AS_FRAUD(click, "TTI Too Short - Possible Click Injection")
  ELSE IF time_difference > 24 HOURS THEN
    FLAG_AS_FRAUD(click, "TTI Too Long - Possible Click Flooding")
  END IF
END FUNCTION

Example 3: Behavioral Heuristics

This logic assesses patterns of behavior that are inconsistent with genuine user engagement. A high frequency of clicks from a single device or user in a short period without corresponding installs or in-app events suggests automated bot activity rather than human interest.

FUNCTION check_behavior(device_id, time_window):
  clicks_in_window = count_clicks(device_id, time_window)
  installs_in_window = count_installs(device_id, time_window)

  IF clicks_in_window > 50 AND installs_in_window == 0 THEN
    FLAG_AS_BOT(device_id)
  END IF
END FUNCTION

📈 Practical Use Cases for Businesses

• Campaign Shielding – Real-time filtering of ad traffic to prevent bots and fraudulent users from consuming the advertising budget, ensuring that spend is allocated toward reaching genuine potential customers.
• Data Integrity – By removing invalid traffic, businesses ensure their analytics platforms reflect true user engagement and conversion rates, leading to more accurate decision-making and performance measurement.
• ROAS Optimization – Eliminating fraudulent installs and clicks improves the return on ad spend (ROAS) by stopping payments for fake users and ensuring that marketing efforts are accurately attributed to real, valuable customers.
• User Acquisition Funnel Protection – Securing the top of the funnel ensures that the users entering the acquisition pipeline are legitimate, preventing skewed metrics in later stages like retention and lifetime value.

Example 1: Geolocation Mismatch Rule

This logic prevents fraud where a click’s IP address location is different from the claimed device or app store location, a common tactic used by bots employing proxies or VPNs to mimic traffic from high-value regions.

FUNCTION check_geo(click_ip_country, store_country):
  IF click_ip_country != store_country THEN
    REJECT(click, "Geolocation Mismatch")
  ELSE
    ACCEPT(click)
  END IF
END FUNCTION

Example 2: New Device Rate Anomaly

This logic identifies device farms or simulators that rapidly create new device IDs to generate fraudulent installs. A sudden, massive spike in installs from “new” devices that have no prior history is a strong indicator of this type of fraud.

FUNCTION check_new_device_rate(traffic_source, time_window):
  total_installs = get_installs(traffic_source, time_window)
  new_device_installs = get_new_device_installs(traffic_source, time_window)
  new_device_percentage = (new_device_installs / total_installs) * 100

  IF new_device_percentage > 90 THEN
    FLAG_AS_FRAUD(traffic_source, "Anomalous New Device Rate")
  END IF
END FUNCTION

🐍 Python Code Examples

This Python function simulates checking a list of incoming click IP addresses against a predefined blocklist of known fraudulent IPs. It’s a fundamental step in filtering out low-quality traffic before it consumes resources.

FRAUD_IP_BLOCKLIST = {"203.0.113.1", "198.51.100.5", "203.0.113.42"}

def filter_fraudulent_ips(click_stream):
  clean_clicks = []
  for click in click_stream:
    if click['ip_address'] not in FRAUD_IP_BLOCKLIST:
      clean_clicks.append(click)
    else:
      print(f"Blocked fraudulent IP: {click['ip_address']}")
  return clean_clicks

# Example usage:
clicks = [
  {'id': 1, 'ip_address': '8.8.8.8'},
  {'id': 2, 'ip_address': '203.0.113.1'}, # Fraudulent IP
  {'id': 3, 'ip_address': '198.18.0.1'}
]
filter_fraudulent_ips(clicks)

This code analyzes click timestamps to detect abnormally high click frequencies from a single user ID, a common sign of bot activity. It helps identify non-human, automated traffic designed to overwhelm ad campaigns.

from collections import defaultdict
from datetime import datetime, timedelta

# Store click timestamps for each user
user_clicks = defaultdict(list)

def detect_click_frequency_anomaly(user_id, click_time_str):
  click_time = datetime.fromisoformat(click_time_str)
  user_clicks[user_id].append(click_time)
  
  # Define the time window and frequency threshold
  time_window = timedelta(minutes=1)
  max_clicks_in_window = 10
  
  # Filter clicks within the last minute
  recent_clicks = [t for t in user_clicks[user_id] if click_time - t <= time_window]
  
  if len(recent_clicks) > max_clicks_in_window:
    print(f"High frequency alert for user {user_id}")
    return True
  return False

# Example usage:
detect_click_frequency_anomaly("user-123", "2025-07-17T11:30:00")
detect_click_frequency_anomaly("user-123", "2025-07-17T11:30:05")
# ... 10 more times in 50 seconds
detect_click_frequency_anomaly("user-123", "2025-07-17T11:30:55")

Types of App store analytics

• First-Party App Store Analytics – These are native tools provided by the app stores themselves, such as Apple’s App Store Connect and the Google Play Console. They offer direct data on impressions, page views, downloads, and sales, providing a baseline for performance and conversion rate analysis.
• Third-Party Attribution Platforms – These are specialized services that offer more granular tracking and cross-channel analysis than native tools. They excel at attributing installs to specific marketing campaigns, ad networks, and even individual ad creatives, which is essential for measuring ROAS and detecting fraud at the source.
• Fraud-Specific Analytics Suites – These platforms are exclusively focused on detecting and preventing ad fraud. They use sophisticated algorithms, machine learning, and vast datasets of known fraud patterns to analyze traffic in real-time and block invalid activity before it results in a paid attribution.
• Behavioral Analytics Tools – While not strictly for fraud detection, these tools analyze in-app user behavior, such as session length, screen flows, and event completion. Anomalies in this data, like immediate drop-offs after install or non-human interaction patterns, can serve as strong indicators of low-quality or fraudulent traffic.