Archives: Glossary Terms

How Fast TV Works

Ad Request → [FAST TV Gateway] → +-----------------------+ → Decision
                                 │ Real-Time Analysis    │
                                 ├-----------------------┤
                                 │ 1. IP Reputation      │
                                 │ 2. Device Fingerprint │
                                 │ 3. Behavioral Check   │
                                 │ 4. Signature Match    │
                                 └-----------------------+
                                         │
                                         ↓
                                 [Fraud Score] → {Allow} or {Block}

🧠 Core Detection Logic

Example 1: Repetitive Click Velocity

This logic prevents click bombing from a single source by tracking click frequency. It is applied at the session or user level to identify non-human, automated clicking behavior. If a user or IP address generates an unnaturally high number of clicks in a short period, it is flagged and blocked.

FUNCTION check_click_velocity(user_id, timeframe_seconds, max_clicks):
  // Get all clicks from user_id within the last timeframe_seconds
  recent_clicks = get_clicks_for_user(user_id, timeframe_seconds)
  
  IF count(recent_clicks) > max_clicks:
    RETURN "BLOCK"
  ELSE:
    RETURN "ALLOW"
  ENDIF

// Usage:
// check_click_velocity("user-123", 60, 5) -> Blocks if user-123 clicked more than 5 times in 60s.

Example 2: Data Center & Proxy Detection

This rule filters out traffic originating from servers and data centers, which are commonly used by bots to mask their origin. It works by checking the click’s IP address against a known database of data center IP ranges. This is a fundamental check in any traffic protection system to eliminate non-human traffic.

FUNCTION is_datacenter_ip(ip_address):
  // Load the list of known data center IP ranges
  datacenter_ranges = load_datacenter_ip_database()
  
  FOR range IN datacenter_ranges:
    IF ip_address IN range:
      RETURN TRUE
    ENDIF
  ENDFOR
  
  RETURN FALSE

// Usage:
// IF is_datacenter_ip("68.183.18.118"):
//   REJECT_TRAFFIC("Reason: Data Center IP")
// ENDIF

Example 3: Geo-Mismatch Heuristic

This logic identifies fraud when there is a mismatch between the user’s stated location (e.g., from browser settings) and their actual location inferred from their IP address. This is effective against bots that use proxies or VPNs to appear as if they are from a high-value country for advertisers.

FUNCTION check_geo_mismatch(ip_address, browser_timezone):
  // Get country from IP address using a GeoIP service
  ip_country = get_country_from_ip(ip_address)
  
  // Get plausible countries for the browser's timezone
  countries_for_timezone = get_countries_from_timezone(browser_timezone)
  
  IF ip_country NOT IN countries_for_timezone:
    // Mismatch detected, flag as suspicious
    RETURN "FLAG_FOR_REVIEW"
  ELSE:
    RETURN "PASS"
  ENDIF

// Example: IP is in Vietnam, but timezone is "America/New_York" -> Mismatch

📈 Practical Use Cases for Businesses

• Campaign Shielding – Prevents ad budgets from being wasted on automated bot clicks by blocking invalid traffic in real time. This ensures that ad spend reaches real potential customers, directly improving ROI.
• Data Integrity – Keeps analytics platforms clean from fraudulent interactions. By filtering out non-human traffic, businesses can trust their metrics like click-through rates and conversion rates to make accurate strategic decisions.
• Lead Generation Filtering – Protects lead forms from being filled with fake or malicious information by bots. This saves sales teams time and resources by ensuring they only engage with genuinely interested prospects.
• Geographic Targeting Enforcement – Ensures that ads intended for a specific region are only clicked by users genuinely located there. It blocks clicks from proxies or VPNs outside the target area, optimizing spend on geographically-sensitive campaigns.

Example 1: Advanced Geofencing Rule

This pseudocode defines a rule to block clicks from outside a campaign’s target countries and also rejects clicks from within the target country if they are using an anonymizing proxy.

// Campaign settings
TARGET_COUNTRIES = ["US", "CA", "GB"]

FUNCTION process_click(click_data):
  ip_geo = get_geolocation(click_data.ip)
  is_proxy = is_known_proxy(click_data.ip)

  IF ip_geo.country NOT IN TARGET_COUNTRIES:
    BLOCK(click_data, "Reason: Out of Geo")
  ELSE IF is_proxy:
    BLOCK(click_data, "Reason: Anonymized Proxy")
  ELSE:
    ACCEPT(click_data)
  ENDIF

Example 2: Session Behavior Scoring

This example scores a user session based on behavior. A session with unnaturally fast clicks and no mouse movement (typical of a simple bot) receives a high fraud score and is blocked.

FUNCTION score_session(session_data):
  score = 0
  
  // Rule 1: Time between page load and first click
  IF session_data.time_to_first_click < 1: // Less than 1 second
    score = score + 40
  ENDIF
  
  // Rule 2: Mouse movement
  IF session_data.mouse_events == 0:
    score = score + 50
  ENDIF
  
  // Rule 3: Click frequency in session
  IF session_data.click_count > 5:
    score = score + (session_data.click_count * 5)
  ENDIF
  
  RETURN score

// Decision logic
session_score = score_session(current_session)
IF session_score > 80:
  BLOCK_USER()
ENDIF

🐍 Python Code Examples

This Python function simulates a basic check to filter out traffic from known bad IP addresses. It reads from a predefined blocklist and returns True if the incoming IP is found, indicating it should be blocked.

# A set of known fraudulent IP addresses
IP_BLOCKLIST = {"203.0.113.14", "198.51.100.22", "192.0.2.101"}

def is_ip_blocked(ip_address):
    """Checks if an IP address is in the global blocklist."""
    if ip_address in IP_BLOCKLIST:
        print(f"Blocking fraudulent IP: {ip_address}")
        return True
    return False

# Example usage:
is_ip_blocked("203.0.113.14") # Returns True

This code example demonstrates how to detect abnormal click frequency from a single source. It maintains a simple in-memory dictionary to track clicks per IP and flags any IP that exceeds a certain threshold within a short time window.

import time

CLICK_LOG = {}
TIME_WINDOW = 10  # seconds
CLICK_THRESHOLD = 5

def is_click_fraud(ip_address):
    """Detects rapid, repetitive clicks from the same IP."""
    current_time = time.time()
    
    # Clean up old entries from the log
    CLICK_LOG[ip_address] = [t for t in CLICK_LOG.get(ip_address, []) if current_time - t < TIME_WINDOW]
    
    # Add the current click timestamp
    CLICK_LOG.setdefault(ip_address, []).append(current_time)
    
    # Check if the click count exceeds the threshold
    if len(CLICK_LOG[ip_address]) > CLICK_THRESHOLD:
        print(f"Fraud detected: High click velocity from {ip_address}")
        return True
    return False

# Example usage:
for _ in range(6):
    is_click_fraud("198.18.0.1") # Will return True on the 6th call

Types of Fast TV

• Rule-Based Filtering – This is the most straightforward type, using a predefined set of static rules to block traffic. It includes blacklists for known fraudulent IP addresses, data centers, or user-agent strings. It is fast and effective against known, unsophisticated threats.
• Heuristic Analysis – This type uses behavioral indicators and “rules of thumb” to score traffic. It analyzes patterns like click velocity, time-on-page, mouse movement, or screen resolution to identify anomalies. It is better at catching newer threats that aren’t on blacklists yet.
• Signature-Based Detection – This method identifies traffic based on unique digital “signatures” associated with known bots or malware. The signature can be a combination of browser properties, header information, and JavaScript footprints. It is highly effective against specific botnets.
• Predictive AI/Machine Learning – The most advanced type uses machine learning models trained on vast datasets of fraudulent and legitimate traffic. It can identify complex, evolving fraud patterns and predict the likelihood of a new, unseen user being a bot based on subtle correlations.

How Fingerprint Analysis Works

  User Click on Ad      +-----------------------+      Analyzed Traffic
──────────────────>│   Data Collection   │──────────────────>
                      +-----------------------+
                           │
                           │ (Browser/Device Attributes: IP, OS, User Agent, etc.)
                           ↓
                      +-----------------------+
                      │ Fingerprint Hashing │
                      +-----------------------+
                           │
                           │ (Unique Fingerprint ID)
                           ↓
  +-------------------------------------------------------------------------+
  │                                   │                                     │
  ↓                                   ↓                                     ↓
+------------------+    +-------------------------+    +---------------------+
│ Anomaly Detection│    │  Behavioral Analysis  │    │   Cross-Referencing │
│ (e.g. VPN, Proxy)│    │(Click Freq., Time on Page)│    │  (Known Fraud DB)   │
+------------------+    +-------------------------+    +---------------------+
  │                                   │                                     │
  └───────────────────┬───────────────────┘
                      ↓
              +-------------------+
              │   Fraud Scoring   │
              +-------------------+
                      │
                      ↓
          +------------------------+
          │ Block / Flag / Allow │
          +------------------------+

🧠 Core Detection Logic

Example 1: Repetitive Click Analysis

This logic identifies a high frequency of clicks originating from the same device fingerprint within a short time frame. It is a fundamental technique in click fraud detection to catch bots programmed to repeatedly click on ads to deplete a competitor’s budget or generate fraudulent revenue.

FUNCTION check_click_frequency(fingerprint_id, campaign_id, time_window):
  // Get all clicks with the same fingerprint and campaign ID within the time window
  click_history = GET_CLICKS(fingerprint_id, campaign_id, time_window)
  
  // Count the number of clicks
  click_count = COUNT(click_history)
  
  // Define the maximum allowed clicks within the window
  threshold = 3
  
  // If the count exceeds the threshold, it's likely fraudulent
  IF click_count > threshold THEN
    RETURN "fraudulent"
  ELSE
    RETURN "legitimate"
  END IF
END FUNCTION

Example 2: Geolocation Mismatch

This logic compares the IP address geolocation with the device’s timezone setting. A significant mismatch can indicate a user is attempting to mask their location using a VPN or proxy, which is a common tactic in sophisticated ad fraud schemes to bypass geo-targeted campaigns.

FUNCTION verify_geolocation(ip_address, device_timezone):
  // Get the geolocation based on the IP address
  ip_geolocation = GET_GEO_FROM_IP(ip_address) // e.g., "America/New_York"
  
  // If the IP's timezone doesn't match the device's timezone, flag it
  IF ip_geolocation.timezone != device_timezone THEN
    // Increase fraud score
    INCREASE_FRAUD_SCORE(ip_address, 10)
    RETURN "suspicious"
  ELSE
    RETURN "consistent"
  END IF
END FUNCTION

Example 3: Bot-Like Behavior Detection

This logic checks for characteristics commonly associated with automated bots, such as the use of headless browsers or outdated browser versions that real users are unlikely to have. This helps filter out non-human traffic designed to perform ad fraud at scale.

FUNCTION detect_bot_behavior(user_agent, browser_properties):
  // Check if the user agent indicates a headless browser
  is_headless = CONTAINS(user_agent, "HeadlessChrome")
  
  // Check if the browser version is unusually old
  is_outdated = browser_properties.version < "Chrome/80"
  
  // Check for inconsistencies in browser plugins
  has_inconsistent_plugins = CHECK_PLUGIN_CONSISTENCY(browser_properties.plugins)
  
  IF is_headless OR is_outdated OR has_inconsistent_plugins THEN
    RETURN "bot-like"
  ELSE
    RETURN "human-like"
  END IF
END FUNCTION

📈 Practical Use Cases for Businesses

• Campaign Shielding: Protects advertising campaigns from bot-driven click fraud, ensuring that ad spend is directed toward genuine human users and not wasted on automated, invalid clicks.
• Lead Generation Filtering: Ensures that leads generated from online forms are from real potential customers by filtering out submissions from bots and fraudulent users, improving the quality of the sales funnel.
• E-commerce Fraud Prevention: Helps identify and block fraudulent transactions by detecting users who employ tactics like card testing or account takeovers, thereby reducing chargebacks and financial losses.
• Content Protection: For businesses with subscription models, fingerprinting can detect account sharing beyond policy limits and prevent users from abusing free trials by creating multiple accounts, thus protecting revenue.
• Analytics Accuracy: By filtering out non-human traffic, businesses can ensure their website analytics are clean and reflect real user engagement, leading to more accurate data for business decision-making.

Example 1: Geofencing Rule

This pseudocode demonstrates a geofencing rule that blocks clicks from users whose IP address location is outside the campaign's target geography. This is crucial for local businesses or region-specific campaigns to ensure their budget is spent on the right audience.

FUNCTION enforce_geofence(user_ip, campaign_target_region):
  user_location = GET_LOCATION_FROM_IP(user_ip)
  
  IF user_location.country NOT IN campaign_target_region.countries THEN
    BLOCK_CLICK(user_ip, "GEO_FENCE_VIOLATION")
    RETURN "Blocked"
  ELSE
    RETURN "Allowed"
  END IF
END FUNCTION

Example 2: Session Scoring Logic

This logic calculates a risk score for a user session based on multiple fingerprint attributes. A high score, indicating multiple suspicious factors, would result in the session being flagged for review or blocked, protecting the advertiser from complex fraud attempts.

FUNCTION calculate_session_risk(fingerprint):
  risk_score = 0
  
  IF fingerprint.is_using_vpn THEN
    risk_score += 40
  END IF
  
  IF fingerprint.browser_is_headless THEN
    risk_score += 50
  END IF
  
  IF fingerprint.timezone_mismatch THEN
    risk_score += 10
  END IF
  
  RETURN risk_score
END FUNCTION

Example 3: Signature Match for Known Bots

This example shows how a system can block traffic by matching a device's fingerprint against a pre-compiled blacklist of known fraudulent signatures. This is a highly efficient way to stop repeat offenders and known botnets.

FUNCTION match_known_bot_signature(device_fingerprint):
  known_bot_signatures = GET_BOT_BLACKLIST()
  
  IF device_fingerprint IN known_bot_signatures THEN
    BLOCK_TRAFFIC(device_fingerprint, "KNOWN_BOT_DETECTED")
    RETURN "Fraudulent"
  ELSE
    RETURN "Not a known bot"
  END IF
END FUNCTION

🐍 Python Code Examples

This code simulates the detection of abnormal click frequency from a single IP address, a common indicator of bot activity. It groups clicks by IP and identifies those exceeding a defined threshold within a short time window.

import pandas as pd
from datetime import timedelta

# Sample click data
data = {'timestamp': ['2023-10-26 10:00:01', '2023-10-26 10:00:02', '2023-10-26 10:00:03', '2023-10-26 10:01:00'],
        'ip_address': ['192.168.1.1', '192.168.1.1', '192.168.1.1', '198.51.100.5']}
clicks = pd.DataFrame(data)
clicks['timestamp'] = pd.to_datetime(clicks['timestamp'])

# Group clicks by IP and count clicks within a 10-second window
click_counts = clicks.groupby('ip_address').rolling('10S', on='timestamp').count()

# Identify IPs with more than 2 clicks in the window as fraudulent
fraudulent_ips = click_counts[click_counts['ip_address'] > 2].index.get_level_values('ip_address')

print(f"Fraudulent IPs detected: {list(set(fraudulent_ips))}")

This example demonstrates how to filter out traffic from suspicious user agents, such as known bots or headless browsers. This is a straightforward way to block low-complexity automated traffic from interacting with ads.

# List of suspicious user agent strings
suspicious_user_agents = ["HeadlessChrome", "PhantomJS", "Selenium"]

def filter_suspicious_user_agents(user_agent_string):
    """Checks if a user agent is in the suspicious list."""
    for agent in suspicious_user_agents:
        if agent in user_agent_string:
            return True
    return False

# Example usage
user_agent = "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) HeadlessChrome/94.0.4606.61 Safari/537.36"
if filter_suspicious_user_agents(user_agent):
    print(f"Suspicious user agent detected: {user_agent}")

This function creates a basic risk score for a click based on a few device attributes. This scoring mechanism can help prioritize which traffic needs further investigation or should be automatically blocked based on a combined risk assessment.

def calculate_traffic_risk_score(click_details):
    """Calculates a risk score based on click attributes."""
    score = 0
    # High-risk country
    if click_details.get("country") == "RU":
        score += 25
    # Known VPN usage
    if click_details.get("is_vpn"):
        score += 50
    # Mismatch between IP and device timezone
    if click_details.get("timezone_mismatch"):
        score += 25
    return score

# Example click data
click_data = {"country": "US", "is_vpn": True, "timezone_mismatch": True}
risk_score = calculate_traffic_risk_score(click_data)
print(f"Traffic risk score: {risk_score}")

if risk_score > 50:
    print("Action: Block traffic")

Types of Fingerprint Analysis

• Device Fingerprinting: Gathers hardware and software data like OS, time zone, screen resolution, and CPU details to create a stable identifier for a physical device. It helps in identifying a device even if different browsers are used.
• Browser Fingerprinting: Focuses on identifying individual browsers by examining attributes like user agent, installed fonts, plugins, and rendering behavior. This is highly effective but can be altered by browser updates or privacy settings.
• Canvas Fingerprinting: A more advanced technique that instructs the browser to render a hidden 2D image or text. Minor variations in how different hardware and software combinations draw the image create a unique and highly accurate fingerprint.
• Audio Fingerprinting: Similar to canvas fingerprinting, this method tests how a device's audio stack processes a sound signal. The resulting waveform is unique to the device's specific hardware and software configuration, providing another layer of identification.
• Behavioral Fingerprinting: This type analyzes patterns of user interaction, such as mouse movements, typing speed, and scrolling behavior, to distinguish between humans and bots. Bots often exhibit robotic, predictable patterns that this method can detect.

How Firewall Protection Works

Incoming Ad Traffic (Click)
           │
           ▼
+----------------------+
│  Firewall Gateway    │
│ (Initial Screening)  │
+----------------------+
           │
           ├─→ [Rule: IP Blacklist?] ───→ Block (Fraudulent)
           │
           ├─→ [Rule: Geo-Mismatch?] ───→ Block (Fraudulent)
           │
           ├─→ [Rule: Known Bot UA?] ───→ Block (Fraudulent)
           │
           ▼
+----------------------+
│  Behavioral Analysis │
+----------------------+
           │
           ├─→ [Heuristic: Click Storm?] ──→ Flag & Block
           │
           ├─→ [Heuristic: No Mouse?]  ──→ Flag & Block
           │
           ▼
+----------------------+
│   Legitimate Traffic │
└─→ (Passed to Site)

🧠 Core Detection Logic

Example 1: IP Filtering and Reputation

This logic checks the incoming click’s IP address against known lists of fraudulent sources. It’s a foundational layer of defense that quickly blocks traffic from data centers, VPNs/proxies, and IPs with a history of malicious activity, which are rarely used by genuine customers.

FUNCTION checkIP(request):
  ip_address = request.getIP()
  
  IF ip_address IS IN global_blacklist:
    RETURN "BLOCK"
  
  IF getIPInfo(ip_address).source == "DataCenter":
    RETURN "BLOCK"
    
  IF getIPInfo(ip_address).is_proxy == TRUE:
    RETURN "BLOCK"
    
  RETURN "PASS"

Example 2: User-Agent Validation

This logic inspects the user-agent (UA) string sent by the browser. Bots often use outdated, inconsistent, or “headless” browser UAs that differ from those of legitimate users. This check identifies non-standard UAs that are common indicators of automated traffic.

FUNCTION checkUserAgent(request):
  user_agent = request.getUserAgent()
  
  IF user_agent IS EMPTY or user_agent IS NULL:
    RETURN "BLOCK"
    
  IF user_agent CONTAINS "HeadlessChrome" OR user_agent IS IN known_bot_uas:
    RETURN "BLOCK"
  
  // Check for inconsistencies, e.g., a mobile UA with a desktop screen resolution
  IF is_inconsistent(user_agent, request.getDeviceInfo()):
    RETURN "BLOCK"
    
  RETURN "PASS"

Example 3: Click Frequency Analysis (Heuristics)

This logic analyzes the timing and frequency of clicks originating from the same device or IP address. A high volume of clicks in an unnaturally short period, or “click stacking,” is impossible for a human and is a strong signal of bot activity which this rule is designed to catch.

FUNCTION checkClickFrequency(request):
  device_id = request.getDeviceID()
  current_time = now()
  
  // Get timestamps of last 5 clicks from this device
  click_history = getClickHistory(device_id, limit=5)
  
  // If more than 3 clicks in the last 10 seconds, block
  clicks_in_10s = count_clicks_within_timespan(click_history, current_time, 10)
  
  IF clicks_in_10s > 3:
    log_event("Click Storm Detected", device_id)
    RETURN "BLOCK"
    
  RETURN "PASS"

📈 Practical Use Cases for Businesses

• Campaign Shielding – Firewall protection actively filters out bot clicks from paid campaigns in real-time. This prevents ad budgets from being wasted on non-human traffic, ensuring that spend is allocated toward reaching genuine potential customers and maximizing return on investment.
• Data Integrity – By blocking fraudulent traffic at the source, firewalls ensure that website analytics and campaign performance data are clean and accurate. This allows businesses to make reliable, data-driven decisions about marketing strategy, budget allocation, and audience targeting without skewed metrics.
• Lead Generation Funnel Protection – For businesses focused on acquiring leads, a firewall prevents bots from submitting fake forms or initiating fraudulent sign-ups. This keeps the sales pipeline clean, reduces the manual effort of sorting through junk leads, and ensures sales teams engage only with legitimate prospects.
• Preserving Retargeting Audiences – Firewalls prevent bots from polluting retargeting lists. By ensuring only genuinely interested users who visit the site are added to the audience, businesses can run more effective and cost-efficient retargeting campaigns that reach people who have shown actual interest.

Example 1: Geofencing Rule

This logic blocks clicks originating from countries outside of the campaign’s target market, preventing budget waste from irrelevant locations often associated with click farms.

FUNCTION applyGeofence(request):
  ip_address = request.getIP()
  country = getCountryFromIP(ip_address)
  
  allowed_countries = ["USA", "CAN", "GBR"]
  
  IF country NOT IN allowed_countries:
    RETURN "BLOCK"
  ELSE:
    RETURN "PASS"

Example 2: Session Scoring Rule

This logic assigns a risk score based on multiple factors. A click from a residential IP might get a low score, while a click from a data center with a mismatched timezone gets a high score. Clicks exceeding a score threshold are blocked.

FUNCTION calculateSessionScore(request):
  score = 0
  
  ip_info = getIPInfo(request.getIP())
  device_info = getDeviceInfo(request.getUserAgent())
  
  IF ip_info.source == "DataCenter":
    score += 50
  
  IF ip_info.is_proxy == TRUE:
    score += 30
    
  IF device_info.is_headless_browser == TRUE:
    score += 60
    
  // Block if score is dangerously high
  IF score >= 80:
    RETURN "BLOCK"
  ELSE:
    RETURN "PASS"

🐍 Python Code Examples

This Python function simulates checking a click’s IP address against a predefined blacklist. It’s a simple yet effective method to filter out traffic from known malicious sources before it consumes any ad budget.

KNOWN_FRAUDULENT_IPS = {"198.51.100.15", "203.0.113.22", "192.0.2.88"}

def block_by_ip(click_ip):
    """Blocks a click if its IP is in the known fraudulent list."""
    if click_ip in KNOWN_FRAUDULENT_IPS:
        print(f"Blocking fraudulent IP: {click_ip}")
        return False
    print(f"Allowing legitimate IP: {click_ip}")
    return True

# Simulate incoming clicks
block_by_ip("8.8.8.8")  # Legitimate
block_by_ip("198.51.100.15") # Fraudulent

This example demonstrates click frequency analysis. The code tracks click timestamps for each user ID and blocks users who click too frequently in a short time, a common sign of non-human bot activity.

from collections import defaultdict
import time

CLICK_HISTORY = defaultdict(list)
TIME_WINDOW_SECONDS = 10
MAX_CLICKS_IN_WINDOW = 4

def is_click_fraud(user_id):
    """Checks for rapid, successive clicks from the same user."""
    current_time = time.time()
    
    # Filter out clicks older than the time window
    CLICK_HISTORY[user_id] = [t for t in CLICK_HISTORY[user_id] if current_time - t < TIME_WINDOW_SECONDS]
    
    # Add the current click timestamp
    CLICK_HISTORY[user_id].append(current_time)
    
    if len(CLICK_HISTORY[user_id]) > MAX_CLICKS_IN_WINDOW:
        print(f"Fraud detected for user {user_id}: Too many clicks.")
        return True
    
    print(f"User {user_id} click is within limits.")
    return False

# Simulate clicks from a user
is_click_fraud("user-123")
is_click_fraud("user-123")
is_click_fraud("user-123")
is_click_fraud("user-123")
is_click_fraud("user-123") # This one will be flagged

Types of Firewall Protection

• Rule-Based Filtering – This is the most fundamental type of firewall. It operates on a strict set of predefined rules, such as blocking specific IP addresses, countries, or device types. It is effective against known and obvious sources of fraudulent traffic but lacks flexibility against new threats.
• Heuristic Analysis – This type uses “rules of thumb” to identify suspicious behavior that deviates from the norm. It analyzes patterns like click velocity, session duration, and mouse movement. For example, it can flag traffic where a click happens faster than a human could realistically react after seeing an ad.
• Behavioral Analysis – A more advanced method that creates a baseline of normal human visitor behavior and flags outliers. It tracks user interactions over time to distinguish between the nuanced patterns of genuine users and the more simplistic, repetitive actions of automated bots.
• Reputation-Based Filtering – This firewall leverages collective intelligence. It uses continuously updated databases of IPs, domains, and device fingerprints that have been previously associated with fraudulent activity across a wide network, allowing one advertiser’s discovery to help protect others.
• Signature-Based Detection – This approach identifies bots by matching their digital “signature”—such as their user-agent string, browser properties, or specific HTTP request headers—against a library of known fraudulent signatures. It is highly effective at stopping bots that have been identified before.

How First touch attribution Works

  User Click ────► [Traffic Security Gateway] ────► Analyze First Touchpoint ────► Decision
                      │                                  │                        │
                      │                                  ├─ IP Reputation        ├─► Allow (Legitimate)
                      │                                  ├─ Device Fingerprint   │
                      └─ [Fraud Signature Database] ◄────┴─ Behavioral Check     └─► Block (Fraudulent)

🧠 Core Detection Logic

Example 1: First-Touch IP Blacklisting

This logic checks the IP address of the first click against a maintained list of known fraudulent IP addresses (e.g., data centers, proxies, botnets). It serves as a simple but highly effective initial filter to block traffic from sources with a history of fraudulent activity.

FUNCTION isFraudulent(click_event):
  BLACKLISTED_IPS = ["1.2.3.4", "5.6.7.8", ...]
  
  IF click_event.ip_address IN BLACKLISTED_IPS:
    RETURN TRUE
  ELSE:
    RETURN FALSE

Example 2: User-Agent Validation

This logic inspects the User-Agent string from the first interaction. It flags traffic from headless browsers, outdated browser versions, or known bot signatures that legitimate users rarely have. This helps filter out simple automated scripts and unsophisticated bots at the entry point.

FUNCTION isBot(click_event):
  KNOWN_BOT_AGENTS = ["PhantomJS", "Selenium", "ScrapyBot"]
  
  FOR agent IN KNOWN_BOT_AGENTS:
    IF agent IN click_event.user_agent:
      RETURN TRUE
      
  IF click_event.user_agent IS NULL OR click_event.user_agent == "":
    RETURN TRUE
    
  RETURN FALSE

Example 3: Timestamp Anomaly (Time-to-Click)

This logic measures the time elapsed between when an ad is served (impression) and when it is clicked. Clicks that occur in less than a second are often indicative of bots, as humans require more time to see, process, and physically click an ad. This first-touch heuristic helps identify non-human speed.

FUNCTION isClickSpeedAnomaly(impression_event, click_event):
  time_to_click = click_event.timestamp - impression_event.timestamp
  
  // Threshold set to 1 second (1000 milliseconds)
  IF time_to_click < 1000:
    RETURN TRUE
  ELSE:
    RETURN FALSE

📈 Practical Use Cases for Businesses

• Budget Protection – Instantly block fraudulent first clicks from bots and click farms, ensuring that advertising spend is only used on potentially genuine customers.
• Lead Quality Improvement – By filtering out non-human traffic at the first touch, businesses ensure that the leads entering their sales funnel are from actual interested users.
• Accurate Campaign Analytics – Prevent fraudulent interactions from inflating click metrics and skewing performance data, leading to a more accurate understanding of campaign effectiveness.
• Affiliate Fraud Prevention – Ensure that credit for generating a new lead is not wrongly given to fraudulent affiliates who use bots to create fake first touches.

Example 1: Geolocation Mismatch Rule

This pseudocode demonstrates a rule that blocks a click if its IP address geolocation does not match the campaign's targeted country. This is a common first-touch defense against click fraud originating from outside the intended advertising region.

FUNCTION validateGeo(click_ip, campaign_target_country):
  user_country = getCountryFromIP(click_ip)
  
  IF user_country != campaign_target_country:
    blockClick()
    logFraud("Geo Mismatch")
  ELSE:
    allowClick()

Example 2: First-Touch Risk Scoring

This pseudocode shows a system that calculates a risk score for each initial click based on multiple factors. If the total score exceeds a certain threshold, the click is blocked, providing a more nuanced approach than a single rule.

FUNCTION calculateRiskScore(click_event):
  score = 0
  
  IF isDataCenterIP(click_event.ip):
    score += 40
    
  IF isHeadlessBrowser(click_event.user_agent):
    score += 30
    
  IF hasRapidClickPattern(click_event.ip):
    score += 30
    
  RETURN score

// Implementation
click_risk = calculateRiskScore(new_click)
IF click_risk >= 50:
  blockClick()

🐍 Python Code Examples

This Python function simulates checking an incoming click's IP address against a predefined set of fraudulent IPs. It's a fundamental first-touch technique to filter out traffic from known bad sources before it consumes resources.

def block_by_ip_blacklist(click_ip, blacklist):
    """Checks if a click's IP is in a known fraud blacklist."""
    if click_ip in blacklist:
        print(f"Blocking fraudulent IP: {click_ip}")
        return True
    return False

# Example Usage
fraudulent_ips = {"192.168.1.101", "10.0.0.5"}
incoming_click_ip = "192.168.1.101"
block_by_ip_blacklist(incoming_click_ip, fraudulent_ips)

This code analyzes a series of clicks to detect rapid, repetitive clicking from a single source within a short timeframe. Such a pattern at the first point of interaction is a strong indicator of bot activity designed to deplete ad budgets.

import time

def detect_click_frequency_anomaly(clicks, ip_address, time_window_seconds=10, max_clicks=5):
    """Detects abnormally high click frequency from a single IP at first touch."""
    now = time.time()
    relevant_clicks = [
        c for c in clicks 
        if c['ip'] == ip_address and (now - c['timestamp']) < time_window_seconds
    ]
    
    if len(relevant_clicks) > max_clicks:
        print(f"Fraud Alert: High frequency from {ip_address}")
        return True
    return False

# Example Usage (timestamps are simplified)
click_stream = [
    {'ip': '8.8.8.8', 'timestamp': time.time() - 2},
    {'ip': '8.8.8.8', 'timestamp': time.time() - 3},
    {'ip': '8.8.8.8', 'timestamp': time.time() - 4},
    {'ip': '8.8.8.8', 'timestamp': time.time() - 5},
    {'ip': '8.8.8.8', 'timestamp': time.time() - 6},
    {'ip': '8.8.8.8', 'timestamp': time.time() - 7}
]
detect_click_frequency_anomaly(click_stream, '8.8.8.8')

Types of First touch attribution

• Session-Based First Touch – This method scrutinizes the very first event in a new user session. If that initial click or interaction is flagged as fraudulent, all subsequent activities within that same session are tainted and can be disregarded, preventing attribution to a compromised session.
• Device-ID First Touch – In mobile advertising, the first interaction from a unique device ID is analyzed. This is crucial for detecting fraud where bots reset other parameters but retain the same device ID, allowing for the blocking of the device itself after the first fraudulent act.

* IP-Centric First Touch - Focuses heavily on the reputation and behavior of the source IP address at the first point of contact. If an IP is identified as part of a data center, a known proxy, or has a history of bot-like activity, it is blocked immediately.

• Campaign-Specific First Touch – With this approach, the system tracks the first time a user interacts with a specific ad campaign. This helps identify fraud targeted at high-value campaigns and prevents bad actors from getting credit for generating what appears to be initial interest in a new promotion.

How Fraud Analytics Works

Incoming Ad Traffic (Clicks, Impressions)
           │
           ▼
+-----------------------+
│ 1. Data Collection    │
│ (IP, UA, Timestamp)   │
+-----------------------+
           │
           ▼
+-----------------------+      +------------------+
│ 2. Real-Time Analysis ├─────▶│ Rule & Model Engine│
│ (Pattern Matching)    │      │ (e.g., ML, Heuristics)│
+-----------------------+      +------------------+
           │
           ▼
+-----------------------+
│ 3. Scoring & Flagging │
│ (Assign Risk Score)   │
+-----------------------+
           │
           └───────────┐
                       ▼
+--------------------+  +---------------------+
│ 4a. Block/Redirect │  │ 4b. Allow & Monitor │
│ (High-Risk)        │  │ (Low-Risk)          │
+--------------------+  +---------------------+

🧠 Core Detection Logic

Example 1: IP Reputation and Blacklisting

This logic involves checking the visitor’s IP address against known lists of fraudulent sources. It’s a foundational layer of traffic protection that filters out traffic from data centers, anonymous proxies, and IPs with a history of malicious activity. This is one of the first checks performed in a traffic security pipeline.

FUNCTION check_ip_reputation(ip_address):
  IF ip_address IN known_datacenter_ips:
    RETURN "BLOCK"
  
  IF ip_address IN global_proxy_blacklist:
    RETURN "BLOCK"
  
  IF ip_address IN historical_fraud_ips:
    RETURN "BLOCK"
  
  RETURN "ALLOW"
END

Example 2: Session Heuristics and Click Velocity

This logic analyzes the timing and frequency of clicks to identify non-human patterns. Bots often click ads much faster or at more regular intervals than a real person would. This heuristic helps detect automated scripts that are programmed to generate a high volume of fake clicks in a short amount of time.

FUNCTION analyze_click_velocity(session_id, click_timestamp):
  // Get previous clicks from the same session
  previous_clicks = get_clicks_by_session(session_id)
  
  // Calculate time since last click
  time_since_last_click = click_timestamp - last_click_timestamp(previous_clicks)
  
  IF time_since_last_click < 2 seconds: // Unnaturally fast click
    RETURN "FLAG_AS_SUSPICIOUS"
    
  // Check for more than 5 clicks in the last minute
  clicks_in_last_minute = count_clicks_in_window(previous_clicks, 60)
  IF clicks_in_last_minute > 5:
    RETURN "FLAG_AS_SUSPICIOUS"
    
  RETURN "PASS"
END

Example 3: User-Agent and Header Anomaly Detection

This logic inspects the HTTP headers of an incoming request, particularly the User-Agent (UA) string, to spot inconsistencies. Fraudsters often use outdated, generic, or mismatched UA strings that don’t align with a legitimate browser or device profile. This check can uncover unsophisticated bots trying to mask their identity.

FUNCTION validate_user_agent(user_agent_string, headers):
  // Check for known bot signatures in the UA string
  IF contains_bot_signature(user_agent_string):
    RETURN "BLOCK"
    
  // Check if the UA is from a browser version that is years out of date
  IF is_obsolete_browser(user_agent_string):
    RETURN "FLAG_AS_SUSPICIOUS"
    
  // Check for mismatches, e.g., a mobile UA with desktop-only headers
  IF header_mismatch(user_agent_string, headers):
    RETURN "FLAG_AS_SUSPICIOUS"
    
  RETURN "PASS"
END

📈 Practical Use Cases for Businesses

• Campaign Shielding – Prevents ad budgets from being wasted on fraudulent clicks generated by bots or competitors, ensuring that ad spend reaches real potential customers.
• Data Integrity – Keeps analytics data clean from non-human traffic, providing businesses with accurate metrics for making informed marketing decisions and calculating ROI.
• Conversion Funnel Protection – Protects lead generation forms and checkout pages from fake submissions and automated attacks, ensuring the sales pipeline is filled with genuine leads.
• Return on Ad Spend (ROAS) Improvement – By filtering out wasteful and fraudulent traffic, fraud analytics helps increase the efficiency of ad campaigns, leading to a higher return on investment.

Example 1: Geofencing Rule

This pseudocode demonstrates a simple geofencing rule. A business running a local campaign in the United States can use this logic to automatically block clicks originating from countries outside its target market, reducing exposure to international click farms.

FUNCTION apply_geo_filter(click_data):
  allowed_countries = ["US", "CA"]
  
  IF click_data.country_code NOT IN allowed_countries:
    // Block the click and log the event
    block_traffic(click_data.ip_address)
    log_event("Blocked click from non-target country: " + click_data.country_code)
    RETURN "BLOCKED"
  
  RETURN "ALLOWED"
END

Example 2: Session Scoring Logic

This pseudocode shows how a session can be scored based on multiple risk factors. Each suspicious event (like using a VPN or having no mouse movement) adds points to a fraud score. If the total score exceeds a threshold, the session is flagged as fraudulent.

FUNCTION calculate_session_score(session_data):
  fraud_score = 0
  
  IF session_data.is_using_vpn == TRUE:
    fraud_score += 40
    
  IF session_data.is_from_datacenter == TRUE:
    fraud_score += 50
    
  IF session_data.has_mouse_movement == FALSE:
    fraud_score += 20
    
  IF session_data.time_on_page < 3 seconds:
    fraud_score += 15
    
  // Decision based on final score
  IF fraud_score >= 50:
    RETURN "HIGH_RISK"
  ELSE IF fraud_score >= 20:
    RETURN "MEDIUM_RISK"
  ELSE:
    RETURN "LOW_RISK"
  
END

🐍 Python Code Examples

This Python function simulates checking a click’s IP address against a predefined blacklist of known fraudulent IPs. It is a fundamental method for instantly blocking traffic from sources that have already been identified as malicious.

# A set of known fraudulent IP addresses for quick lookups
FRAUDULENT_IPS = {"198.51.100.5", "203.0.113.10", "192.0.2.123"}

def filter_by_ip_blacklist(click_ip):
    """
    Checks if a given IP address is in the blacklist.
    Returns True if the IP should be blocked, otherwise False.
    """
    if click_ip in FRAUDULENT_IPS:
        print(f"Blocking fraudulent IP: {click_ip}")
        return True
    print(f"Allowing valid IP: {click_ip}")
    return False

# Example Usage
filter_by_ip_blacklist("203.0.113.10")
filter_by_ip_blacklist("8.8.8.8")

This code example demonstrates how to detect abnormally high click frequency from a single source within a short time frame. It helps identify automated bots that are programmed to click ads repeatedly, a pattern unlikely to be produced by a human user.

from collections import defaultdict
import time

# Dictionary to store timestamps of clicks for each IP
click_logs = defaultdict(list)
TIME_WINDOW = 60  # seconds
CLICK_THRESHOLD = 10  # max clicks allowed in the time window

def detect_click_frequency_anomaly(ip_address):
    """
    Analyzes click frequency to identify potential bot activity.
    Returns True if the frequency is abnormally high.
    """
    current_time = time.time()
    
    # Add current click timestamp and remove old ones
    click_logs[ip_address].append(current_time)
    click_logs[ip_address] = [t for t in click_logs[ip_address] if current_time - t < TIME_WINDOW]
    
    # Check if click count exceeds the threshold
    if len(click_logs[ip_address]) > CLICK_THRESHOLD:
        print(f"High frequency detected from IP: {ip_address}")
        return True
        
    return False

# Example Usage
for _ in range(12):
    detect_click_frequency_anomaly("192.168.1.100")

Types of Fraud Analytics

• Rule-Based Analytics – This method uses predefined rules and thresholds to filter traffic. For instance, it might automatically block any clicks from known data center IP addresses or those that occur with impossibly high frequency. It is effective against common, known fraud tactics.
• Behavioral Analytics – This type focuses on analyzing user behavior patterns to distinguish real users from bots. It tracks metrics like mouse movements, scroll depth, and time spent on a page. Deviations from typical human behavior patterns are flagged as suspicious.
• Predictive Analytics – Using historical data and machine learning, this approach predicts the likelihood that a future click or transaction will be fraudulent. It identifies subtle, high-risk patterns that may not violate a specific rule but are indicative of emerging fraud tactics.
• Link Analysis – This technique is used to uncover relationships between seemingly disconnected data points. For example, it can identify fraud rings by finding multiple user accounts that share the same device ID, IP address, or payment information, revealing coordinated fraudulent activity.
• Anomaly Detection – This type establishes a baseline of normal traffic behavior and then monitors for any deviations. A sudden spike in traffic from a new geography or an unusual jump in click-through rates without a corresponding increase in conversions would be flagged as an anomaly for further investigation.

How Fraud Compliance Works

Incoming Ad Traffic → [ Pre-Filter ] → [ Deep Analysis Engine ] → +----------------+ → [ Reporting & Logging ]
      │                   │                     │                  │                │
      │                   │                     │                  │   ┌─ Allow ───┘
      │                   │                     │                  └─┤
      │                   └─ (IP Blacklist,     │                    └─ Block
      │                        User Agent)      │
      └─────────────────────────────────────────┘
                                           (Behavioral, Heuristics,
                                            Session Scoring)

🧠 Core Detection Logic

Example 1: IP Filtering and Reputation

This logic checks the incoming IP address against a known database of fraudulent or suspicious IPs, such as those associated with data centers, VPNs, or botnets. It’s a fundamental first line of defense in traffic protection, blocking obvious non-human traffic at the entry point.

FUNCTION checkIpReputation(request):
  ip = request.getIpAddress()
  
  IF ip IN known_datacenter_ips OR ip IN proxy_blocklist:
    RETURN "BLOCK"
  
  IF ip.getReputationScore() < 20: // Score out of 100
    RETURN "BLOCK"
    
  RETURN "ALLOW"

Example 2: Session Click Velocity Heuristics

This type of logic analyzes user behavior within a single session to identify patterns impossible for a genuine user. A high frequency of clicks in an abnormally short time frame is a strong indicator of an automated script or bot, rather than a potential customer.

FUNCTION analyzeSessionVelocity(session):
  clicks = session.getClickCount()
  session_duration_seconds = session.getDuration()

  // Prevent division by zero for very short sessions
  IF session_duration_seconds < 1:
    session_duration_seconds = 1
  
  clicks_per_second = clicks / session_duration_seconds

  IF clicks > 5 AND clicks_per_second > 2:
    RETURN "FLAG_AS_FRAUD"
    
  RETURN "PASS"

Example 3: Geo Mismatch Detection

This logic compares the geographical location derived from the user's IP address with other location data, such as timezone settings from the browser or language preferences. A significant mismatch often indicates the use of a proxy or VPN to mask the user's true origin, a common tactic in ad fraud.

FUNCTION checkGeoMismatch(request):
  ip_location = getLocationFromIp(request.ip) // e.g., "Germany"
  browser_timezone = request.headers.get("Browser-Timezone") // e.g., "America/New_York"

  // If timezone does not align with the IP's country
  IF ip_location == "Germany" AND "America" in browser_timezone:
    RETURN "BLOCK_SUSPICIOUS_GEO"
    
  RETURN "ALLOW"

📈 Practical Use Cases for Businesses

• Campaign Shielding – Automatically block clicks and impressions from known bots and fraudulent sources, ensuring advertising budgets are spent on reaching real, potential customers.
• Data Integrity – By filtering out non-human traffic, businesses ensure their analytics (like CTR and conversion rates) reflect genuine user engagement, leading to more accurate decision-making.
• ROAS Improvement – Preventing wasted ad spend on fraudulent clicks directly improves Return on Ad Spend (ROAS), as the budget is more efficiently allocated to traffic that can actually convert.
• Lead Generation Quality – For businesses focused on acquiring leads, fraud compliance filters out fake form submissions generated by bots, ensuring the sales team receives higher-quality, legitimate leads.

Example 1: Geofencing Rule

A business targeting a local customer base can use geofencing to automatically block any ad interaction originating from outside its specified service regions.

// Rule: Only allow traffic from the United States and Canada
FUNCTION applyGeoFence(request):
  allowed_countries = ["US", "CA"]
  user_country = getCountryFromIp(request.ip)

  IF user_country NOT IN allowed_countries:
    REJECT_INTERACTION(reason="Outside Target Geography")
  ELSE:
    ACCEPT_INTERACTION()

Example 2: Session Scoring Logic

A system can score each user session based on multiple risk factors. A session accumulating too many risk points is flagged as fraudulent and blocked.

// Logic: Score session based on risk signals
FUNCTION scoreSession(session):
  risk_score = 0
  
  IF session.uses_vpn():
    risk_score += 40
    
  IF session.is_headless_browser():
    risk_score += 50
    
  IF session.click_count > 10 in 5_seconds:
    risk_score += 30

  IF risk_score > 60:
    BLOCK_SESSION(score=risk_score)
  ELSE:
    PASS_SESSION(score=risk_score)

🐍 Python Code Examples

This Python function simulates checking for abnormal click frequency from a single IP address. If an IP generates more than a set number of clicks in a short interval, it's flagged as suspicious, a common behavior for bots.

# A simple in-memory store for tracking click timestamps
ip_click_tracker = {}
from collections import deque
import time

def is_click_frequency_abnormal(ip_address, click_limit=5, time_window_seconds=10):
    """Checks if an IP has an unusually high click frequency."""
    current_time = time.time()
    
    if ip_address not in ip_click_tracker:
        ip_click_tracker[ip_address] = deque()

    # Remove timestamps older than the time window
    while (ip_click_tracker[ip_address] and 
           current_time - ip_click_tracker[ip_address] > time_window_seconds):
        ip_click_tracker[ip_address].popleft()

    ip_click_tracker[ip_address].append(current_time)
    
    if len(ip_click_tracker[ip_address]) > click_limit:
        print(f"ALERT: Abnormal click frequency detected for IP {ip_address}")
        return True
        
    return False

# Simulation
is_click_frequency_abnormal("192.168.1.10")
is_click_frequency_abnormal("192.168.1.10")
is_click_frequency_abnormal("192.168.1.10")
is_click_frequency_abnormal("192.168.1.10")
is_click_frequency_abnormal("192.168.1.10")
is_click_frequency_abnormal("192.168.1.10") # This will trigger the alert

This example demonstrates filtering traffic based on the User-Agent string. The function checks if the User-Agent matches any known patterns associated with bots or automated scripts and blocks them accordingly.

def filter_suspicious_user_agents(user_agent_string):
    """Filters out requests from known bot-related user agents."""
    SUSPICIOUS_PATTERNS = [
        "bot",
        "crawler",
        "spider",
        "headlesschrome" # Often used by automation scripts
    ]
    
    lower_ua = user_agent_string.lower()
    
    for pattern in SUSPICIOUS_PATTERNS:
        if pattern in lower_ua:
            print(f"BLOCK: Suspicious user agent detected: {user_agent_string}")
            return False # Block request
            
    print(f"ALLOW: User agent appears valid: {user_agent_string}")
    return True # Allow request

# Simulation
filter_suspicious_user_agents("Mozilla/5.0 (compatible; Googlebot/2.1; +http://www.google.com/bot.html)")
filter_suspicious_user_agents("Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36")

Types of Fraud Compliance

• Rule-Based Compliance – This type uses a static set of predefined rules to filter traffic. For example, it automatically blocks all traffic from a specific list of IP addresses or any interaction that occurs outside of business hours. It is fast and straightforward but not adaptable to new threats.
• Behavioral Compliance – This method focuses on analyzing patterns of user behavior to identify anomalies. It tracks metrics like click speed, mouse movement, and time-on-page to distinguish between genuine human actions and automated bot activity, which often follows rigid, non-human patterns.
• Reputational Compliance – This approach relies on third-party data to assess the reputation of an incoming connection. It checks the IP address, device ID, or user agent against global databases of known fraudulent actors, blocking traffic that has a poor reputation score.
• Heuristic Compliance – Using algorithmic rules of thumb (heuristics), this type identifies suspicious activity that doesn't fit expected norms but isn't on a known blacklist. An example is flagging a user who clicks on 15 ads within a 10-second window as highly unlikely to be legitimate.

How Fraud Detection Algorithms Works

Incoming Ad Traffic → [ Data Collection ] → [ Algorithm Analysis ] → +----------------+ → [ Feedback Loop ]
(Clicks/Impressions)  (IP, UA, Behavior)   (Pattern/Anomaly Scan) │ Decision Logic │       (Model Tuning)
                                                                 ├────────────────┤
                                                                 │  Allow (Human) │
                                                                 └─ Block (Bot)  → To Quarantine/Blacklist

🧠 Core Detection Logic

Example 1: IP-Based Filtering Rule

This logic identifies and blocks traffic originating from sources known to be associated with non-human activity, such as data centers or servers. It’s a foundational layer of defense that filters out obvious bot traffic before it can interact with ads.

FUNCTION check_ip(ip_address):
  // Predefined list of known data center IP ranges
  datacenter_ips = ["198.51.100.0/24", "203.0.113.0/24"]

  IF ip_address in datacenter_ips:
    RETURN "BLOCK" // Traffic is from a server, not a real user
  ELSE:
    RETURN "ALLOW"
END FUNCTION

Example 2: Behavioral Heuristics

This type of logic analyzes user behavior to spot patterns impossible for a typical human user. An impossibly short time between a page loading and a click on an ad is a strong indicator of an automated script, as humans require time to process information.

FUNCTION check_behavior(time_on_page, clicks_in_session):
  // Set thresholds for suspicious behavior
  MIN_TIME_THRESHOLD = 0.5 // seconds
  MAX_CLICKS_THRESHOLD = 5 // clicks per minute

  IF time_on_page < MIN_TIME_THRESHOLD:
    RETURN "FLAG_AS_BOT" // Click was too fast to be human

  IF clicks_in_session > MAX_CLICKS_THRESHOLD:
    RETURN "FLAG_AS_FRAUD" // Unnaturally high click frequency

  RETURN "LEGITIMATE"
END FUNCTION

Example 3: Geo Mismatch Anomaly

This logic flags inconsistencies between a user’s stated location (e.g., from browser settings or language) and their technical location (derived from their IP address). Such mismatches are common when fraudsters use proxies or VPNs to disguise their origin.

FUNCTION check_geo_mismatch(ip_geo, browser_timezone):
  // Map timezones to expected countries
  TIMEZONE_TO_COUNTRY_MAP = {
    "America/New_York": "US",
    "Europe/London": "GB",
    "Asia/Tokyo": "JP"
  }

  expected_country = TIMEZONE_TO_COUNTRY_MAP.get(browser_timezone)
  actual_country = get_country_from_ip(ip_geo)

  IF expected_country IS NOT NULL and actual_country != expected_country:
    RETURN "SUSPICIOUS" // User's IP location doesn't match their system timezone
  ELSE:
    RETURN "OK"
END FUNCTION

📈 Practical Use Cases for Businesses

• Campaign Shielding – Actively block bots and fraudulent users in real-time to prevent them from clicking on ads. This directly protects pay-per-click (PPC) budgets from being wasted on traffic that will never convert, ensuring ad spend is directed toward genuine potential customers.
• Data Integrity – Ensure marketing analytics are based on clean, human-generated data. By filtering out invalid traffic, businesses can trust their metrics like click-through rates and conversion rates, leading to more accurate insights and smarter strategic decisions.
• ROAS Optimization – Improve Return On Ad Spend (ROAS) by eliminating wasteful expenditures on fraudulent interactions. When algorithms ensure that ads are primarily shown to real users, the overall effectiveness and profitability of advertising campaigns increase significantly.
• Lead Generation Quality Control – Prevent fake or bot-driven form submissions on landing pages. This saves sales and marketing teams time by ensuring the lead database is filled with genuine prospects, not automated junk data from malicious sources.

Example 1: Geofencing Rule

This logic prevents ad spend from being wasted on clicks originating from outside a campaign’s targeted geographical area. It’s a simple but effective rule for local or regional businesses.

// USE CASE: A local bakery in Paris targets customers only in France.
FUNCTION apply_geofence(click_data):
  ALLOWED_COUNTRY = "FR"
  
  IF click_data.country_code != ALLOWED_COUNTRY:
    // Block the click and do not charge the advertiser
    LOG_EVENT("Blocked out-of-geo click from: " + click_data.ip)
    RETURN "BLOCK"
  ELSE:
    RETURN "ALLOW"
END FUNCTION

Example 2: Session Risk Scoring

This logic aggregates multiple risk factors into a single score to make a more nuanced decision. A user might exhibit one or two slightly odd behaviors, but a high cumulative score strongly indicates fraud.

// USE CASE: Evaluate multiple signals to determine traffic authenticity.
FUNCTION calculate_risk_score(session_data):
  score = 0
  
  IF session_data.is_from_datacenter:
    score += 50
  
  IF session_data.has_mismatched_timezone:
    score += 20
    
  IF session_data.click_frequency > 10: // per minute
    score += 30
  
  // A score over 60 is considered high-risk
  IF score > 60:
    RETURN "HIGH_RISK"
  ELSE:
    RETURN "LOW_RISK"
END FUNCTION

🐍 Python Code Examples

This function simulates checking the click frequency from a single IP address. If an IP exceeds a certain number of clicks in a short time, it’s flagged as suspicious, a common sign of bot activity.

# In-memory store to track clicks per IP
CLICK_LOG = {}
TIME_WINDOW = 60  # seconds
CLICK_THRESHOLD = 15

def is_suspicious_frequency(ip_address):
    import time
    current_time = time.time()
    
    # Get timestamps for this IP, or an empty list if new
    timestamps = CLICK_LOG.get(ip_address, [])
    
    # Filter out clicks older than our time window
    recent_timestamps = [t for t in timestamps if current_time - t < TIME_WINDOW]
    
    # Add the current click time
    recent_timestamps.append(current_time)
    
    # Update the log
    CLICK_LOG[ip_address] = recent_timestamps
    
    # Check if the number of recent clicks exceeds the threshold
    if len(recent_timestamps) > CLICK_THRESHOLD:
        print(f"Flagged IP: {ip_address} for high frequency.")
        return True
        
    return False

# --- Simulation ---
# is_suspicious_frequency("192.168.1.10") returns False
# ...after 16 quick calls...
# is_suspicious_frequency("192.168.1.10") returns True

This code filters traffic based on the User-Agent string. It blocks requests from known bot signatures or from user agents that are blank or malformed, which is a common characteristic of low-quality bots.

KNOWN_BOT_AGENTS = ["Scrapy", "DataMiner", "FriendlyBot"]

def filter_by_user_agent(user_agent_string):
    # Block if user agent is missing or empty
    if not user_agent_string:
        print("Blocked: Missing User-Agent")
        return False
        
    # Block if user agent matches a known bot signature
    for bot in KNOWN_BOT_AGENTS:
        if bot in user_agent_string:
            print(f"Blocked: Known bot signature found - {bot}")
            return False
            
    print("Allowed: User-Agent appears valid.")
    return True

# --- Simulation ---
# filter_by_user_agent("Mozilla/5.0 ... Chrome/94.0") returns True
# filter_by_user_agent("Scrapy/2.5.0 (+https://scrapy.org)") returns False
# filter_by_user_agent(None) returns False

Types of Fraud Detection Algorithms

• Rule-Based Systems – This is the most straightforward type, using manually set rules to flag fraud. For instance, a rule might block all clicks from a specific IP address or flag any user who clicks an ad more than 10 times in one minute. They are fast but not adaptable to new threats.
• Statistical Anomaly Detection – This method uses statistical models to establish a baseline of normal traffic behavior. It then flags deviations from this baseline as potential fraud. For example, a sudden, unexpected spike in clicks from a country not usually in your top traffic sources would be flagged as an anomaly.
• Supervised Machine Learning – These algorithms are trained on historical datasets that have been labeled as either “fraudulent” or “legitimate.” The model learns the characteristics of each category and then uses that knowledge to classify new, incoming traffic. It is highly accurate but requires large amounts of labeled data.
• Unsupervised Machine Learning – This type of algorithm does not require labeled data. Instead, it analyzes a dataset and clusters traffic into different groups based on their inherent characteristics. It can identify new types of fraud by spotting clusters of traffic that behave differently from the norm, even if that pattern has never been seen before.
• Heuristic and Behavioral Analysis – This approach analyzes patterns of user interaction, such as mouse movements, keystroke dynamics, and browsing speed. It distinguishes humans from bots by identifying behaviors that are difficult for automated scripts to mimic, like erratic mouse movements or natural typing rhythms.

How Fraud Intelligence Works

Incoming Traffic (Click/Impression)
          │
          ▼
+---------------------+
│   Data Collection   │
│ (IP, UA, Behavior)  │
+---------------------+
          │
          ▼
+---------------------+
│  Real-Time Analysis │
│ (Rules, Heuristics) │
+---------------------+
          │
          ▼
+---------------------+
│  Risk Scoring       │
+---------------------+
          │
          ▼
      /───────
    /  Decision   
  /    (Block?)     
                   /
     _________ /
          │
          ├───> [Allow] ───> Protected Asset (Ad/Site)
          │
          └─> [Block] ───> Log & Report

🧠 Core Detection Logic

Example 1: IP Reputation and Filtering

This logic checks the incoming IP address against known lists of fraudulent or suspicious sources. It is a foundational layer of traffic protection, designed to quickly filter out obvious bad actors, such as those originating from data centers, VPNs, or previously flagged bot networks before they can interact with an ad.

FUNCTION check_ip_reputation(ip_address):
  // Check against known bad IP lists
  IF ip_address IN data_center_ips_list THEN
    RETURN "BLOCK - Data Center"
  
  IF ip_address IN vpn_proxy_list THEN
    RETURN "BLOCK - VPN/Proxy Detected"
  
  IF ip_address IN historical_fraud_ips THEN
    RETURN "BLOCK - Previously Identified Fraud"

  // If no negative matches, allow
  RETURN "ALLOW"
END FUNCTION

Example 2: Session Heuristics and Velocity Checks

This logic analyzes the timing and frequency of user actions within a single session to identify non-human behavior. Bots often perform actions much faster or more methodically than humans. This type of check helps catch automated scripts that may have bypassed initial IP filters.

FUNCTION analyze_session_velocity(session_data):
  click_timestamps = session_data.get_clicks()
  
  // Check for abnormally fast clicks
  IF count(click_timestamps) > 1 THEN
    time_between_clicks = click_timestamps - click_timestamps
    IF time_between_clicks < 0.5 seconds THEN
      RETURN "FLAG - Click Velocity Too High"
  END IF
  
  // Check for too many clicks in a short window
  time_window = 60 seconds
  clicks_in_window = count_clicks_in_last_n_seconds(session_data, time_window)
  IF clicks_in_window > 20 THEN
    RETURN "FLAG - High Frequency Activity"
  END IF

  RETURN "PASS"
END FUNCTION

Example 3: Geographic Mismatch

This logic verifies that the user’s apparent geographic location is consistent with their device settings and the campaign’s targeting parameters. A significant mismatch, such as a device language set to Russian appearing from a US IP address, can be a strong indicator of a proxy or a compromised device trying to mask its true origin.

FUNCTION check_geo_mismatch(ip_location, device_language, campaign_target_country):
  
  // Check if click is outside the campaign's target area
  IF ip_location.country != campaign_target_country THEN
    RETURN "BLOCK - Geo-Targeting Mismatch"
  END IF
  
  // Check for suspicious language/location inconsistencies
  IF ip_location.country == "USA" AND device_language IN ["zh-CN", "ru-RU", "vi-VN"] THEN
    RETURN "FLAG - Suspicious Geo-Language Inconsistency"
  END IF

  RETURN "PASS"
END FUNCTION

📈 Practical Use Cases for Businesses

• Campaign Shielding – Real-time blocking of clicks from bots, competitors, and click farms ensures that advertising budgets are spent only on reaching genuine potential customers, maximizing return on ad spend (ROAS).
• Lead Generation Integrity – Filters out fake form submissions and sign-ups generated by bots. This provides sales teams with higher-quality leads, saving time and resources by eliminating the need to chase down fraudulent contacts.
• Clean Analytics – By preventing invalid traffic from reaching a website, Fraud Intelligence ensures that analytics platforms report accurate user engagement metrics. This allows businesses to make reliable, data-driven decisions about their marketing strategies and website optimization.
• E-commerce Protection – Protects online stores from inventory-hoarding bots, protects against fraudulent chargebacks, and ensures that product recommendation algorithms are based on real user behavior, not skewed by automated traffic.

Example 1: Dynamic IP Blocking Rule

This logic automatically blocks an IP address after it exhibits a pattern of low-quality engagement, such as multiple clicks on an ad without any conversions or meaningful on-site interaction. This is a common tactic for protecting PPC campaigns from budget-wasting clicks.

// Rule: Block an IP after 3 clicks with no conversions in 24 hours
FUNCTION dynamic_ip_block(ip_address, click_history):
  
  clicks_from_ip = click_history.filter(ip == ip_address, time > now() - 24h)
  conversions_from_ip = clicks_from_ip.filter(event_type == 'conversion')

  IF count(clicks_from_ip) >= 3 AND count(conversions_from_ip) == 0 THEN
    ADD ip_address TO permanent_block_list
    LOG "IP blocked due to high clicks, zero conversions."
    RETURN "BLOCKED"
  END IF

  RETURN "MONITORING"

Example 2: Session Behavior Scoring

This example scores a user session based on multiple behavioral indicators. A session that appears too short or lacks typical human interaction (like scrolling) receives a high fraud score and may be blocked or flagged for review. This protects against bots that are sophisticated enough to load a page but fail to mimic human behavior.

// Rule: Score a session based on its behavior
FUNCTION score_session_behavior(session):
  fraud_score = 0
  
  // Penalty for very short session duration
  IF session.duration_seconds < 2 THEN
    fraud_score += 40
  END IF
  
  // Penalty for no mouse movement
  IF session.mouse_events == 0 THEN
    fraud_score += 30
  END IF
  
  // Penalty for instant form submission (honeypot)
  IF session.form_fill_time_ms < 500 THEN
    fraud_score += 50
  END IF
  
  RETURN fraud_score // e.g., if score > 75, block

🐍 Python Code Examples

This Python function simulates the detection of abnormally high click frequency from a single IP address. It checks if the number of clicks within a defined time window (e.g., 5 clicks in 60 seconds) exceeds a set threshold, a common sign of bot activity.

from datetime import datetime, timedelta

def check_click_frequency(ip_address, click_logs, threshold=5, window_seconds=60):
    """Checks if an IP has exceeded the click frequency threshold."""
    now = datetime.now()
    time_window_start = now - timedelta(seconds=window_seconds)
    
    recent_clicks = [
        log['timestamp'] for log in click_logs.get(ip_address, []) 
        if log['timestamp'] > time_window_start
    ]
    
    if len(recent_clicks) > threshold:
        print(f"ALERT: High frequency detected for IP {ip_address}")
        return True
        
    return False

# Example Usage:
clicks = {
    "98.123.45.67": [
        {"timestamp": datetime.now() - timedelta(seconds=i)} for i in range(10)
    ]
}
check_click_frequency("98.123.45.67", clicks)

This code provides a simple filter to identify and block traffic from user agents known to be associated with bots or malicious scrapers. It iterates through a list of known bad signatures and checks if any are present in the provided user agent string.

def is_known_bot(user_agent_string):
    """Checks a user agent string against a list of known bot signatures."""
    known_bot_signatures = [
        "AhrefsBot", "SemrushBot", "DotBot", "MegaIndex",
        "python-requests", "Scrapy", "headless-chrome"
    ]
    
    for signature in known_bot_signatures:
        if signature.lower() in user_agent_string.lower():
            print(f"BOT DETECTED: User agent '{user_agent_string}' matches signature '{signature}'.")
            return True
            
    return False

# Example Usage:
ua_human = "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"
ua_bot = "Mozilla/5.0 (compatible; AhrefsBot/7.0; +http://ahrefs.com/robot/)"
is_known_bot(ua_human) # Returns False
is_known_bot(ua_bot)   # Returns True

Types of Fraud Intelligence

• Rule-Based Intelligence – This type uses a predefined set of static rules to identify fraud. For example, a rule might automatically block all clicks coming from known data center IP addresses or from countries not included in a campaign’s targeting. It is fast and effective against known threats.
• Heuristic Intelligence – This method analyzes behavior to find anomalies that suggest automation. It looks for patterns that fall outside the norm of human behavior, such as clicking faster than a human possibly could or visiting pages in a perfectly linear, machine-like sequence.
• Signature-Based Intelligence – This approach identifies fraud by matching incoming traffic against a database of known “signatures” of bad actors. A signature could be a specific IP address, a device fingerprint, or a particular user-agent string that has been previously associated with fraudulent activity.
• Behavioral Intelligence – Focuses on how a user interacts with a page to distinguish humans from bots. It analyzes signals like mouse movements, scroll depth, and keyboard strokes. The absence or unnatural pattern of these interactions is a strong indicator of automated, non-human traffic.
• Reputation-Based Intelligence – This type leverages collective data to determine the trustworthiness of an IP address, device, or domain. If an IP address has a history of fraudulent activity across a network of protected sites, its reputation score will be low, and it can be preemptively blocked.

How Fraud Risk Assessment Works

Incoming Traffic → [ Data Collection ] → [ Feature Extraction ] → [ Risk Scoring Engine ] → [ Decision Logic ] ┬─> Allow Traffic
(Click/Impression) │                   │                    │                      │                     └─> Block/Flag Traffic
                   └───────────────────┴────────────────────┴──────────────────────┴───────────────────────┘
                                                Feedback Loop (Model Retraining)

🧠 Core Detection Logic

Example 1: IP Reputation and Filtering

This logic checks the incoming user’s IP address against continuously updated databases of known fraudulent sources. It is a fundamental, first-line defense in traffic protection, effective at blocking traffic from data centers, known proxies, and botnets.

FUNCTION assess_ip(ip_address):
  // Check against known datacenter, proxy, and malicious IP lists
  IF ip_address IN KNOWN_FRAUD_IP_LIST:
    RETURN { status: 'BLOCK', reason: 'IP on blocklist' }

  // Check against TOR exit nodes
  IF is_tor_node(ip_address):
    RETURN { status: 'BLOCK', reason: 'TOR network detected' }

  RETURN { status: 'ALLOW' }

Example 2: Session and Behavioral Heuristics

This logic analyzes user behavior within a single session to identify non-human patterns. It’s effective against simple bots that fail to mimic natural user engagement, such as inhumanly fast clicks or a complete lack of mouse movement before an action.

FUNCTION assess_session(session_data):
  // Rule: Clicks per minute are too high
  IF session_data.clicks_per_minute > 20:
    INCREASE_RISK_SCORE(30)

  // Rule: Time between page load and click is too short
  IF session_data.time_to_click_seconds < 1:
    INCREASE_RISK_SCORE(40)

  // Rule: No mouse movement detected before click event
  IF session_data.mouse_movement_events == 0:
    INCREASE_RISK_SCORE(25)

  RETURN calculate_final_risk()

Example 3: Geographic Mismatch Rule

This logic cross-references different geographic signals to detect attempts to spoof location. It's useful for identifying fraudsters trying to bypass geo-targeted ad campaigns by using proxies or VPNs, ensuring ad spend is focused on the intended regions.

FUNCTION assess_geo(ip_geo, browser_timezone, browser_language):
  // Compare IP location with browser's timezone
  IF ip_geo.country != browser_timezone.country_code:
    RETURN { status: 'FLAG', reason: 'IP/Timezone mismatch' }

  // Compare IP location with browser's language settings
  IF ip_geo.country == 'USA' AND browser_language NOT IN ['en-US', 'es-US']:
    INCREASE_RISK_SCORE(15)

  RETURN { status: 'ALLOW' }

📈 Practical Use Cases for Businesses

• Campaign Shielding – Fraud Risk Assessment identifies and blocks invalid clicks and impressions in real time, preventing bots and bad actors from depleting pay-per-click (PPC) advertising budgets. This ensures that ad spend is directed toward genuine potential customers.
• Lead Generation Filtering – By analyzing user behavior and source data, the system filters out fake or automated form submissions. This cleans the sales pipeline, saving time and resources by ensuring sales teams only engage with legitimate leads.

- Analytics Purification – It removes non-human traffic from analytics data. This provides businesses with accurate metrics on user engagement, conversion rates, and campaign performance, leading to better strategic decisions and improved return on ad spend (ROAS).

• Brand Safety – The assessment prevents ads from being displayed on low-quality or fraudulent websites (domain spoofing), protecting the brand's reputation and ensuring it is associated with legitimate and relevant content.

Example 1: Geofencing for Local Campaigns

A local business wants to ensure its ads are only shown to users within a specific country. The following logic blocks traffic originating from outside the targeted geographic area, which is a common tactic used by click farms.

PROCEDURE check_campaign_geo(user_ip, campaign_target_country):
  user_country = get_country_from_ip(user_ip)

  IF user_country != campaign_target_country:
    block_request("Geographic mismatch")
    log_event("Blocked click from " + user_country)
  ELSE:
    allow_request()
  END IF
END PROCEDURE

Example 2: Session Score for Engagement Quality

An e-commerce site wants to distinguish between genuinely interested shoppers and bots that click ads but show no engagement. This logic assigns a score based on session behavior; a low score indicates likely fraud.

FUNCTION calculate_session_score(session_events):
  score = 0
  // Reward for human-like behavior
  IF session_events.scrolled_page:
    score = score + 10
  IF session_events.time_on_page > 5_seconds:
    score = score + 15
  IF session_events.mouse_moved_over_product:
    score = score + 25

  // Penalize for bot-like behavior
  IF session_events.clicks > 5 AND session_events.time_on_page < 3_seconds:
    score = score - 50

  RETURN score
END FUNCTION

🐍 Python Code Examples

This code simulates checking for abnormally high click frequency from a single user ID, a common sign of bot activity. It flags users who perform an unrealistic number of clicks within a short time window.

from collections import defaultdict
from datetime import datetime, timedelta

# Store click timestamps for each user
user_clicks = defaultdict(list)
CLICK_LIMIT = 10
TIME_WINDOW_SECONDS = 60

def is_click_frequency_suspicious(user_id):
    """Checks if a user's click frequency is too high."""
    now = datetime.now()
    user_clicks[user_id].append(now)

    # Filter out old clicks outside the time window
    time_limit = now - timedelta(seconds=TIME_WINDOW_SECONDS)
    recent_clicks = [t for t in user_clicks[user_id] if t > time_limit]
    user_clicks[user_id] = recent_clicks

    if len(recent_clicks) > CLICK_LIMIT:
        print(f"Suspicious activity from {user_id}: {len(recent_clicks)} clicks in {TIME_WINDOW_SECONDS}s")
        return True
    return False

# Simulation
is_click_frequency_suspicious("user-123") # Returns False
# Simulate rapid clicks
for _ in range(15):
    is_click_frequency_suspicious("user-456") # Will return True after 11th click

This example demonstrates how to filter traffic based on a user-agent string. It checks if the user agent belongs to a known bot or a non-standard browser commonly used in automated scripts.

KNOWN_BOT_AGENTS = [
    "Bot/1.0",
    "DataScraper/2.1",
    "HeadlessChrome" # Often used in automation
]

def is_user_agent_a_bot(user_agent_string):
    """Checks if a user agent matches a known bot signature."""
    if not user_agent_string:
        print("Blocking request: Missing User-Agent")
        return True

    for bot_signature in KNOWN_BOT_AGENTS:
        if bot_signature.lower() in user_agent_string.lower():
            print(f"Blocking bot request with agent: {user_agent_string}")
            return True

    return False

# Simulation
is_user_agent_a_bot("Mozilla/5.0 (Windows NT 10.0; Win64; x64) ...") # False
is_user_agent_a_bot("DataScraper/2.1 (compatible; http://example.com)") # True
is_user_agent_a_bot(None) # True

Types of Fraud Risk Assessment

• Rule-Based Assessment
  This method uses a predefined set of static rules to identify fraud. For example, a rule might block all clicks originating from a specific IP address or flag any session with more than 10 clicks in one minute. It is fast and straightforward but less effective against sophisticated bots.
• Heuristic Assessment
  This approach uses experience-based techniques and "rules of thumb" to detect anomalies. Unlike rigid rules, heuristics can identify behavior that is suspicious but not definitively fraudulent, such as clicks occurring too quickly after a page loads. This method provides flexibility but can lead to more false positives.
• Behavioral Assessment
  This type focuses on analyzing patterns in user interaction to distinguish between human and non-human behavior. It evaluates metrics like mouse movements, scroll speed, and keystroke dynamics. This method is effective at catching sophisticated bots that can mimic device and network properties but fail to replicate human interaction convincingly.
• Reputational Assessment
  This type evaluates traffic based on the historical reputation of its source data points, such as the IP address, device ID, or domain. An IP address with a history of sending spam or participating in DDoS attacks would be considered high-risk, effectively stopping known bad actors at the door.
• Machine Learning-Based Assessment
  This advanced method uses algorithms to analyze vast datasets and identify complex, evolving fraud patterns that are invisible to rule-based or heuristic systems. It adapts over time, learning from new data to improve its detection accuracy against emerging threats, though it requires significant data and computational power.

How Fraudulent Activity Works

[Ad Interaction] → [Data Collection] → [Signature & Heuristic Analysis] → [Behavioral Profiling] → [Scoring Engine] ┬─> [Valid Traffic]
      │                                                                                                           └─> [Block & Alert]
      └───────────────────────────────────< Feedback Loop & Model Retraining >────────────────────────────────────┘

🧠 Core Detection Logic

Example 1: IP-Based Rules

This logic filters traffic based on the reputation and characteristics of the IP address. It is a foundational layer of fraud detection, effective at blocking known bad actors and traffic from suspicious sources like data centers, which are not typically used by genuine customers.

FUNCTION check_ip(ip_address):
  // Block IPs from known data centers
  IF ip_address IN data_center_ip_list THEN
    RETURN "BLOCK"

  // Block IPs with poor reputation scores
  reputation = get_ip_reputation(ip_address)
  IF reputation.score < 0.2 THEN
    RETURN "BLOCK"
  
  // Block IPs on a manual blacklist
  IF ip_address IN manual_blacklist THEN
    RETURN "BLOCK"
    
  RETURN "ALLOW"

Example 2: Session Heuristics

This logic analyzes the behavior of a user within a single session to identify non-human patterns. It focuses on the timing and frequency of events, which can reveal automation that is too fast, too consistent, or too predictable to be human.

FUNCTION analyze_session(session_data):
  // Check for abnormally fast clicks after page load
  time_to_first_click = session_data.first_click_timestamp - session_data.page_load_timestamp
  IF time_to_first_click < 2 SECONDS THEN
    RETURN "FLAG_AS_SUSPICIOUS"

  // Check for high frequency of clicks from the same user
  click_count = GET_CLICKS_IN_WINDOW(session_data.user_id, 1_MINUTE)
  IF click_count > 10 THEN
    RETURN "FLAG_AS_SUSPICIOUS"
    
  RETURN "PASS"

Example 3: Behavioral Anomaly Detection

This more advanced logic tracks user interactions, such as mouse movements or touch events, to build a behavioral profile. It detects fraud by identifying sessions that lack the natural, subtle variations of human behavior, a strong indicator of sophisticated bots.

FUNCTION analyze_behavior(interaction_events):
  // Check for lack of mouse movement before a click
  mouse_events_count = count(events WHERE type = "mousemove")
  IF mouse_events_count < 3 THEN
    RETURN "HIGH_RISK"

  // Analyze click path and element interaction
  click_path = get_interaction_path(events)
  IF path_is_robotic(click_path) THEN // e.g., perfectly straight lines, instant jumps
    RETURN "HIGH_RISK"
    
  RETURN "LOW_RISK"

📈 Practical Use Cases for Businesses

• Campaign Shielding – Actively blocks invalid clicks and impressions in real-time, preventing fraudulent traffic from consuming ad budgets and ensuring that ad spend is directed toward genuine, potential customers.
• Data Integrity – Filters out bot-generated noise from analytics platforms. This provides a clear and accurate view of campaign performance, enabling better decision-making and optimization based on real user engagement.
• ROAS Optimization – Improves Return On Ad Spend (ROAS) by eliminating wasteful spending on fraudulent interactions. By ensuring ads are served to humans, businesses increase the likelihood of achieving meaningful conversions and higher-value outcomes.
• Lead Generation Cleansing – Prevents fraudulent form submissions on landing pages. This keeps customer relationship management (CRM) systems clean from fake leads, saving sales teams time and effort by ensuring they only follow up on legitimate inquiries.

Example 1: Geofencing Rule

This pseudocode demonstrates a geofencing rule that blocks clicks from countries not targeted by a specific campaign, a common method for filtering out irrelevant and often fraudulent traffic.

FUNCTION check_geo(click_data, campaign_rules):
  user_country = get_country_from_ip(click_data.ip)
  
  IF user_country NOT IN campaign_rules.targeted_countries:
    log_event("Blocked click from non-targeted country:", user_country)
    RETURN "BLOCK"
  
  RETURN "ALLOW"

Example 2: Session Scoring Logic

This example shows a simplified session scoring system that aggregates risk factors to determine if a user is fraudulent. Multiple low-risk signals might be tolerated, but a combination of high-risk indicators will trigger a block.

FUNCTION calculate_fraud_score(session):
  score = 0
  
  IF session.is_from_datacenter:
    score += 50
    
  IF session.user_agent IN known_bot_signatures:
    score += 40
    
  IF session.click_frequency > 15_per_minute:
    score += 20
    
  IF score > 60:
    RETURN "FRAUDULENT"
  ELSE:
    RETURN "VALID"

🐍 Python Code Examples

This Python function simulates checking for abnormally high click frequency from a single IP address within a short time frame. It's a common technique to catch simple bots or click farm activity.

# A simple in-memory store for tracking click timestamps
ip_click_log = {}
from collections import deque
import time

# Time window in seconds and click limit
TIME_WINDOW = 60
CLICK_THRESHOLD = 15

def is_click_flood(ip_address):
    """Checks if an IP has exceeded the click threshold in the time window."""
    current_time = time.time()
    
    # Get or create a deque for the IP
    if ip_address not in ip_click_log:
        ip_click_log[ip_address] = deque()
    
    ip_log = ip_click_log[ip_address]
    
    # Add current click timestamp
    ip_log.append(current_time)
    
    # Remove old timestamps that are outside the window
    while ip_log and ip_log <= current_time - TIME_WINDOW:
        ip_log.popleft()
        
    # Check if click count exceeds the threshold
    if len(ip_log) > CLICK_THRESHOLD:
        print(f"Fraudulent activity detected from IP: {ip_address}")
        return True
        
    return False

# --- Simulation ---
# is_click_flood("123.45.67.89") -> False
# # Rapid clicks from the same IP
# for _ in range(20):
#     is_click_flood("123.45.67.89") -> True on the 16th call

This example demonstrates filtering traffic based on suspicious User-Agent strings. Bots often use generic, outdated, or inconsistent user agents that can be identified and blocked.

SUSPICIOUS_USER_AGENTS = [
    "bot",
    "crawler",
    "spider",
    "headlesschrome", # Often used in automation scripts
    "okhttp" # A common HTTP library used in bots
]

def is_suspicious_user_agent(user_agent_string):
    """Checks if a user agent string contains suspicious keywords."""
    if not user_agent_string:
        return True # Empty user agent is highly suspicious

    ua_lower = user_agent_string.lower()
    for keyword in SUSPICIOUS_USER_AGENTS:
        if keyword in ua_lower:
            print(f"Suspicious user agent detected: {user_agent_string}")
            return True
            
    return False

# --- Simulation ---
# legitimate_ua = "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36"
# bot_ua = "Mozilla/5.0 (compatible; MyCustomBot/1.0; +http://www.example.com/bot.html)"
# is_suspicious_user_agent(legitimate_ua) -> False
# is_suspicious_user_agent(bot_ua) -> True

Types of Fraudulent Activity

• Click Spam – This involves repeated, automated, or manual clicks on an ad by a bot or a low-wage worker with no real interest in the ad's content. Its purpose is to drain an advertiser’s budget or inflate a publisher's earnings. Detection focuses on click frequency and timing anomalies.
• Impression Fraud – This type of fraud generates fake ad impressions by loading ads on pages or in locations that are never seen by real users. Techniques include 1x1 pixel stuffing, ad stacking (layering multiple ads on top of each other), and auto-refreshing pages, all designed to inflate impression counts.
• Botnet Traffic – This uses a network of compromised computers (a botnet) to simulate human-like traffic at a massive scale. This is considered Sophisticated Invalid Traffic (SIVT) because bots can mimic mouse movements, browsing patterns, and other human behaviors, making it harder to detect than simpler fraud types.
• Domain Spoofing – This tactic deceives advertisers by misrepresenting a low-quality or fraudulent website as a legitimate, high-traffic premium site in the ad exchange. Advertisers believe their ads are running on a reputable site, but they are actually being displayed on an irrelevant or unsafe one.
• Ad Injection – This method uses browser extensions or malware to insert ads into a website without the publisher’s permission. These ads can replace the publisher’s legitimate ads or appear on pages where no ads were intended, diverting revenue and creating a poor user experience.