Archives: Glossary Terms

How Conversion rate Works

Incoming Ad Traffic → [Click Analysis Engine] → User Behavior Tracking
      │                       │                             │
      │                       │                             └─ Session Data (Time on page, interactions)
      │                       │
      └─ [IP & Fingerprint Data] → Compare Against Known Fraud Signatures
                                    │
                                    ↓
                     [Conversion Rate Monitoring] ─────┐
                                    │                    │
          (Traffic Segment: Campaign/Source/Geo)         │
                                    │                    │
         (Is Conversion Rate ≈ 0% or Anomalous?) │
                                    │                    │
                                    Yes                  No
                                    │                    │
                                    ↓                    ↓
                          [FLAG AS FRAUDULENT]      [ALLOW TRAFFIC]
                                    │
                                    └─ Block IP & Update Rules

🧠 Core Detection Logic

Example 1: Traffic Source Conversion Anomaly

This logic identifies publishers or traffic sources that send a high volume of clicks but generate zero or statistically insignificant conversions. Since bots and fraudulent users do not complete valuable actions, a near-zero conversion rate from a specific source is a strong indicator of invalid traffic.

FUNCTION check_source_conversion (source_id, time_window):
  clicks = GET_CLICKS(source_id, time_window)
  conversions = GET_CONVERSIONS(source_id, time_window)

  IF clicks > 1000 AND conversions < 1:
    FLAG_AS_FRAUD(source_id)
    BLOCK_SOURCE(source_id)
    ALERT("High click, zero conversion anomaly detected for source: " + source_id)
  ENDIF

Example 2: Geographic Conversion Mismatch

This rule flags traffic from geographic locations that suddenly spike in volume without a corresponding rise in conversions. Click farms are often concentrated in specific regions. This logic compares the conversion rate of a new, high-volume geo-location against the campaign's historical average to spot fraud.

FUNCTION check_geo_conversion (campaign_id, country_code):
  geo_clicks = GET_CLICKS_BY_GEO(campaign_id, country_code)
  geo_conversions = GET_CONVERSIONS_BY_GEO(campaign_id, country_code)
  
  IF geo_clicks > 500:
    geo_conversion_rate = (geo_conversions / geo_clicks) * 100
    historical_avg_rate = GET_HISTORICAL_AVG_CONVERSION_RATE(campaign_id)

    IF geo_conversion_rate < (historical_avg_rate * 0.1):  // If rate is less than 10% of average
      FLAG_AS_FRAUDULENT_GEO(country_code)
      ALERT("Suspiciously low conversion from new high-traffic geo: " + country_code)
    ENDIF
  ENDIF

Example 3: Time-to-Conversion Analysis

This heuristic identifies fraudulent activity by measuring the time between a click and a conversion event. Extremely short or impossibly long durations can indicate automation. For instance, a "conversion" that occurs less than a second after the click is likely a bot, not a human user.

FUNCTION analyze_time_to_conversion (session):
  click_time = session.click_timestamp
  conversion_time = session.conversion_timestamp
  
  time_diff_seconds = conversion_time - click_time

  // Flag if conversion is too fast (e.g., under 2 seconds) 
  // or suspiciously long (e.g., over 24 hours).
  IF time_diff_seconds < 2 OR time_diff_seconds > 86400:
    MARK_CONVERSION_AS_SUSPICIOUS(session.id)
    INVESTIGATE_USER(session.user_id)
    ALERT("Time-to-conversion anomaly detected: " + time_diff_seconds + " seconds")
  ENDIF

📈 Practical Use Cases for Businesses

• Campaign Shielding – Automatically block traffic sources and publishers that send high volumes of clicks with no conversions, preserving ad spend for channels that deliver actual customers.
• Lead Quality Filtration – Prevent fake lead form submissions by identifying and blocking IPs and user agents that exhibit bot-like behavior, such as filling forms instantly or generating numerous low-quality leads.
• Retargeting List Purification – Ensure retargeting audiences consist of genuine, interested users by excluding traffic that clicked on initial ads but never showed any on-site engagement or conversion intent.
• Return on Ad Spend (ROAS) Optimization – Improve ROAS by focusing budget on keywords, campaigns, and demographics that demonstrate healthy conversion rates, while cutting spend on those flagged for fraudulent, non-converting traffic.

Example 1: Publisher Cut-Off Rule

A business running a display ad campaign can automatically pause sending budget to a specific publisher if its traffic fails to meet a minimum conversion threshold over a set period.

RULE publisher_cutoff:
  FOR EACH publisher IN active_campaign.publishers:
    publisher_clicks = COUNT_CLICKS(publisher.id, last_7_days)
    publisher_conversions = COUNT_CONVERSIONS(publisher.id, last_7_days)

    IF publisher_clicks > 2000 AND publisher_conversions == 0:
      PAUSE_SPEND(publisher.id)
      NOTIFY_MANAGER("Publisher " + publisher.id + " paused due to zero conversions.")
    ENDIF

Example 2: Suspicious Lead Scoring

A lead generation business can score incoming leads based on conversion behavior. Leads from IPs with a history of many clicks but no prior sales are given a lower score and flagged for manual review.

FUNCTION score_lead_quality(lead_data):
  ip_history = GET_IP_HISTORY(lead_data.ip_address)
  
  total_clicks = ip_history.total_clicks
  total_conversions = ip_history.total_conversions
  
  lead_score = 100 // Start with a perfect score

  IF total_clicks > 50 AND total_conversions < 1:
    lead_score = lead_score - 50 // Penalize for high non-converting activity
    
  IF lead_score < 60:
    lead_data.status = "Requires Manual Review"
  
  RETURN lead_data

🐍 Python Code Examples

This function simulates checking a dictionary of traffic sources for conversion rate anomalies. It identifies sources with a significant number of clicks but no corresponding conversions, a common sign of bot traffic.

def find_zero_conversion_sources(traffic_data):
    """
    Identifies traffic sources with high clicks and zero conversions.
    
    Args:
      traffic_data: A dict where keys are source IDs and values are
                    dicts with 'clicks' and 'conversions'.
                    
    Returns:
      A list of fraudulent source IDs.
    """
    fraudulent_sources = []
    CLICK_THRESHOLD = 500  # Minimum clicks to be considered significant

    for source_id, metrics in traffic_data.items():
        if metrics.get('clicks', 0) > CLICK_THRESHOLD and metrics.get('conversions', 0) == 0:
            print(f"Flagged Source: {source_id} - Clicks: {metrics['clicks']}, Conversions: 0")
            fraudulent_sources.append(source_id)
            
    return fraudulent_sources

# Example Usage
traffic_report = {
    'publisher_A': {'clicks': 150, 'conversions': 5},
    'publisher_B': {'clicks': 2500, 'conversions': 0},
    'publisher_C': {'clicks': 300, 'conversions': 8},
    'publisher_D': {'clicks': 1200, 'conversions': 0},
}
find_zero_conversion_sources(traffic_report)

This script calculates the conversion rate for different campaigns and flags those that fall significantly below an expected average. This helps advertisers spot underperforming or potentially fraudulent campaigns quickly.

def flag_low_conversion_campaigns(campaign_stats, min_threshold_rate=0.5):
    """
    Flags campaigns with conversion rates below a minimum threshold.

    Args:
      campaign_stats: A list of dicts, each representing a campaign.
      min_threshold_rate: The minimum conversion rate percentage.
    """
    for campaign in campaign_stats:
        clicks = campaign.get('clicks', 0)
        conversions = campaign.get('conversions', 0)
        
        if clicks > 0:
            conversion_rate = (conversions / clicks) * 100
            if conversion_rate < min_threshold_rate:
                print(f"ALERT: Campaign '{campaign['name']}' has a low conversion rate: {conversion_rate:.2f}%")
        else:
            print(f"INFO: Campaign '{campaign['name']}' has no clicks.")

# Example Usage
campaign_data = [
    {'name': 'Summer_Sale', 'clicks': 10000, 'conversions': 150}, # 1.5%
    {'name': 'New_Product_Launch', 'clicks': 5000, 'conversions': 10}, # 0.2%
    {'name': 'Brand_Awareness_Q3', 'clicks': 20000, 'conversions': 5} # 0.025%
]
flag_low_conversion_campaigns(campaign_data, min_threshold_rate=0.5)

Types of Conversion rate

• Macro vs. Micro Conversions Analysis
  This approach distinguishes between primary goals (Macro), like a sale, and secondary actions (Micro), like a newsletter signup. Fraudulent traffic typically fails to complete either, but analyzing both provides deeper insight into user intent and helps separate low-quality human traffic from automated bots.
• Time-to-Conversion (TTC) Analysis
  This method measures the duration between the initial click and the conversion event. Abnormally fast conversions (e.g., under a few seconds) or extremely delayed ones can indicate bot activity or attribution fraud. Legitimate users exhibit more varied and plausible timeframes.
• Conversion Path Analysis
  This technique examines the sequence of pages a user visits before converting. Bots often follow simplistic, linear paths or directly access a conversion page without natural navigation. Deviations from common, logical user journeys can be flagged as suspicious.
• New vs. Returning User Conversion Rates
  Systems can segment conversion rates between first-time visitors and returning users. A high volume of "new users" with a near-zero conversion rate can signal a bot attack, as fraudulent traffic is often designed to appear as new visitors for each session.
• Geographic Conversion Rate Monitoring
  This involves tracking conversion rates by country, region, or city. A sudden influx of clicks from a specific, non-target location with no corresponding conversions is a classic sign of a click farm or a localized botnet, allowing for quick geographic blocking.

How Conversion Tracking Works

+-------------------+      +----------------------+      +---------------------+      +---------------------+
|   User Clicks Ad  |  ->  |  Click Data Captured |  ->  | User Lands on Site  |  ->  | Conversion Event    |
+-------------------+      +----------------------+      +---------------------+      +---------------------+
                             │ (IP, User Agent, TS)                                      │ (e.g., Purchase)
                             │
                             └───────────┐
                                         ↓
+-----------------------------+      +------------------------------+      +-------------------------+
| Cross-Reference with        |  ->  | Identify Anomalies           |  ->  | Flag/Block Fraudulent   |
| Conversion Data             |      | (High Clicks, No Conversion) |      | Source (IP, etc.)       |
+-----------------------------+      +------------------------------+      +-------------------------+

🧠 Core Detection Logic

Example 1: Click-Conversion Mismatch

This logic identifies sources that generate a high volume of clicks without any corresponding valuable actions (conversions). It is a fundamental method for detecting non-human bot traffic or low-quality publishers whose traffic does not engage with the site content. This helps in quickly cutting spend on fraudulent sources.

RULE check_click_conversion_ratio(source_id):
  clicks = get_clicks_from(source_id, last_24_hours)
  conversions = get_conversions_from(source_id, last_24_hours)

  IF clicks > 100 AND conversions == 0:
    FLAG source_id as "Suspicious"
    ADD_TO_REVIEW_LIST(source_id)
  END IF

Example 2: Time-to-Convert Anomaly

This rule flags conversions that happen too quickly after a click. A human user typically requires a reasonable amount of time to read a page, fill out a form, or complete a purchase. A conversion that occurs within a few seconds is almost certainly automated and indicates sophisticated bot activity designed to mimic legitimate users.

RULE analyze_conversion_time(session):
  click_time = session.click_timestamp
  conversion_time = session.conversion_timestamp
  time_diff = conversion_time - click_time

  IF time_diff < 5 seconds:
    FLAG session.ip_address as "High-Risk Bot"
    BLOCK_IP(session.ip_address)
  END IF

Example 3: Geographic Mismatch

This logic checks if the location of the click matches the location where the conversion action is recorded. While minor discrepancies can occur due to network routing, significant mismatches (e.g., a click from one country and a conversion from another) often point to the use of proxies or VPNs to mask the true origin of fraudulent traffic.

RULE validate_geo_location(click_ip, conversion_ip):
  click_geo = get_geolocation(click_ip)
  conversion_geo = get_geolocation(conversion_ip)

  IF click_geo.country != conversion_geo.country:
    SCORE_AS_FRAUD(click_ip, level="Medium")
    LOG_FOR_ANALYSIS(click_ip, conversion_geo)
  END IF

📈 Practical Use Cases for Businesses

• Campaign Shielding – Automatically block IPs and devices showing high click-to-conversion discrepancies, preventing them from seeing and clicking on ads, thus saving the ad budget for genuine users.
• Lead Quality Assurance – By analyzing post-click behavior, businesses can filter out leads generated by bots or fraudulent users who complete forms with fake information, ensuring the sales team receives only high-quality leads.
• Publisher Performance Analysis – Advertisers can use conversion data to evaluate the quality of traffic from different publishers or ad placements, cutting ties with those that consistently deliver non-converting or fraudulent clicks.
• Return on Ad Spend (ROAS) Optimization – By eliminating spend on fraudulent clicks that never convert, conversion tracking ensures that advertising budgets are directed toward channels and campaigns that deliver real results, directly improving ROAS.

Example 1: Publisher Traffic Scoring

This pseudocode demonstrates how a business might score traffic from different publishers based on their conversion performance. Publishers with a consistently low conversion rate are flagged, and their traffic can be automatically limited or blocked entirely.

FUNCTION score_publisher_traffic(publisher_id):
    clicks = query_db("SELECT COUNT(*) FROM clicks WHERE publisher_id = ? AND time > NOW() - 7d")
    conversions = query_db("SELECT COUNT(*) FROM conversions WHERE publisher_id = ? AND time > NOW() - 7d")

    IF clicks > 500:
        conversion_rate = (conversions / clicks) * 100
        IF conversion_rate < 0.1:
            SET_PUBLISHER_STATUS(publisher_id, "LOW_QUALITY")
        END IF
    END IF

Example 2: Session Scoring for Bot-Like Behavior

This example shows logic for scoring a user session based on behavior. A session with many page views but no conversion-related events (like adding an item to a cart or visiting a pricing page) is marked as suspicious, which helps identify sophisticated bots that mimic browsing but have no real intent.

FUNCTION score_session(session_data):
    score = 0
    IF session_data.time_on_site < 10:
        score += 2
    IF session_data.page_views > 20 AND session_data.has_conversion_event == FALSE:
        score += 5
    IF session_data.source.is_known_proxy == TRUE:
        score += 10

    IF score > 12:
        BLOCK_IP(session_data.ip_address)
    END IF

🐍 Python Code Examples

This Python function simulates checking for an abnormally high click frequency from a single IP address within a short time frame, which is a common indicator of bot activity. Traffic from IPs exceeding the threshold would be flagged as fraudulent.

# In-memory store for recent clicks for demonstration purposes
CLICK_LOGS = {}
from collections import defaultdict
import time

# Group clicks by IP
IP_CLICKS = defaultdict(list)
TIME_WINDOW_SECONDS = 60  # 1 minute
CLICK_THRESHOLD = 20

def is_suspicious_frequency(ip_address):
    """Checks if an IP has an unusually high click frequency."""
    current_time = time.time()
    
    # Filter out clicks older than the time window
    IP_CLICKS[ip_address] = [t for t in IP_CLICKS[ip_address] if current_time - t < TIME_WINDOW_SECONDS]
    
    # Add current click timestamp
    IP_CLICKS[ip_address].append(current_time)
    
    # Check if click count exceeds the threshold
    if len(IP_CLICKS[ip_address]) > CLICK_THRESHOLD:
        print(f"Fraud Warning: IP {ip_address} exceeded {CLICK_THRESHOLD} clicks in {TIME_WINDOW_SECONDS} seconds.")
        return True
        
    return False

# Example Usage
is_suspicious_frequency("192.168.1.100") # Returns False
# Simulate 25 rapid clicks
for _ in range(25):
    is_suspicious_frequency("198.51.100.2")

This script analyzes a list of click events to identify sessions with classic signs of fraud: a high number of clicks from an IP but no corresponding conversion events. This helps filter out automated traffic that isn't driving actual business value.

def analyze_session_for_conversion(click_events):
    """
    Analyzes click data to find IPs with high clicks and no conversions.
    A click_event is a dictionary like {'ip': '...', 'converted': True/False}
    """
    ip_stats = defaultdict(lambda: {'clicks': 0, 'conversions': 0})

    for event in click_events:
        ip = event['ip']
        ip_stats[ip]['clicks'] += 1
        if event['converted']:
            ip_stats[ip]['conversions'] += 1
            
    suspicious_ips = []
    for ip, stats in ip_stats.items():
        if stats['clicks'] > 10 and stats['conversions'] == 0:
            suspicious_ips.append(ip)
            
    return suspicious_ips

# Example data
clicks = [
    {'ip': '203.0.113.5', 'converted': False},
    {'ip': '203.0.113.5', 'converted': False},
    {'ip': '198.51.100.2', 'converted': True},
    {'ip': '203.0.113.5', 'converted': False},
]
# Simulate 10 more non-converting clicks from the suspicious IP
for _ in range(10):
    clicks.append({'ip': '203.0.113.5', 'converted': False})

fraudulent_sources = analyze_session_for_conversion(clicks)
print(f"Suspicious IPs based on conversion analysis: {fraudulent_sources}")

Types of Conversion Tracking

• Server-to-Server (S2S) Tracking – This method involves the advertiser's server communicating directly with the ad platform's server to record a conversion. It is more secure and reliable than browser-based methods, making it harder for fraudsters to generate fake conversion signals, as it doesn't rely on client-side scripts that can be manipulated.
• Event-Based Tracking – Instead of just tracking a final sale, this method monitors a series of smaller user actions (events) like "add to cart," "viewed product," or "started checkout." This provides deeper behavioral insight, helping to distinguish legitimate user journeys from the unnatural, linear paths often taken by bots.
• Offline Conversion Tracking – This tracks conversions that happen offline, such as a phone call or an in-store purchase, after an online ad click. By importing sales data from a CRM, businesses can verify that clicks are leading to real-world revenue, making it difficult for online-only fraud to go undetected.
• View-Through Conversion (VTC) Tracking – VTC attributes a conversion to an ad that was seen but not clicked. In fraud detection, monitoring VTCs can uncover impression fraud, where bots generate massive numbers of fake ad views. Unusually high VTC rates with no corresponding click-through conversions can signal this type of malicious activity.

How Cost Control Works

Incoming Ad Click → [+ Data Collection] → [± Rule Engine] → [? Analysis] → [✓ Legitimate] → Ad Display
                                │              │             └─→ [✗ Fraudulent] → Block & Log
                                │              └─→ Behavioral Scoring
                                └─→ IP, User Agent, Timestamp

🧠 Core Detection Logic

Example 1: IP Address Velocity Capping

This logic prevents a single source from depleting an ad budget through rapid, repeated clicks. It operates at the traffic entry point by tracking the number of clicks from each IP address within a specific time frame and blocking any that exceed a predefined threshold.

// Rule: Block an IP if it generates more than 5 clicks in 1 minute.
FUNCTION on_click(request):
  ip = request.get_ip()
  timestamp = get_current_time()

  IF click_log.count(ip, within_last_minute) > 5 THEN
    BLOCK_TRAFFIC(ip)
    LOG_EVENT("Velocity_Fraud", ip)
    RETURN "blocked"
  ELSE
    RECORD_CLICK(ip, timestamp)
    RETURN "allowed"
  ENDIF

Example 2: Geo-Behavioral Mismatch

This logic identifies fraud by detecting inconsistencies between a user’s stated location (via IP geolocation) and their browser or device settings (e.g., language, timezone). It’s effective against bots trying to spoof their location to match campaign targets.

// Rule: Flag traffic if IP country does not match browser language.
FUNCTION on_click(request):
  ip_geo = get_geolocation(request.get_ip())
  browser_lang = request.get_header("Accept-Language")

  // Example: IP is from Germany, but browser language is Russian
  IF ip_geo.country != "Russia" AND browser_lang.starts_with("ru") THEN
    FLAG_FOR_REVIEW(request.get_ip(), "Geo-Mismatch")
    RETURN "suspicious"
  ELSE
    RETURN "allowed"
  ENDIF

Example 3: Session Engagement Scoring

This logic assesses the quality of a click by measuring post-click engagement. Low-quality traffic, such as from bots, often results in an immediate bounce (very short session duration). This rule helps filter out clicks that show no genuine user interest.

// Rule: Flag clicks with a session duration of less than 1 second.
FUNCTION after_click(session_data):
  session_id = session_data.id
  duration = session_data.end_time - session_data.start_time

  IF duration < 1000 THEN // less than 1000 milliseconds
    score = get_fraud_score(session_id)
    score.increase(50) // Increase fraud score
    LOG_EVENT("Low_Engagement", session_id, duration)
  ENDIF

📈 Practical Use Cases for Businesses

• Campaign Shielding – Automatically blocks clicks from known fraudulent sources like data centers and proxy networks, preserving the budget for genuine audiences.
• Lead Quality Assurance – Filters out bot-generated form submissions and sign-ups that originate from fraudulent clicks, ensuring the sales team receives clean, actionable leads.
• ROAS Optimization – Improves return on ad spend by preventing budget waste on traffic that has no chance of converting, allowing for more accurate performance metrics.
• Geographic Targeting Enforcement – Blocks clicks from outside a campaign's targeted regions, preventing budget drain from irrelevant locations and sophisticated location-spoofing bots.

Example 1: Geofencing Rule

A business targeting customers only in the United States uses this logic to block all traffic from other countries, protecting its budget from irrelevant international clicks.

// Logic: Allow traffic only from the specified country.
FUNCTION check_geo(request):
  allowed_country = "US"
  user_country = get_geolocation(request.ip).country

  IF user_country != allowed_country THEN
    BLOCK_CLICK(request.ip, "Geo-Block")
    RETURN False
  ELSE
    RETURN True
  ENDIF

Example 2: Session Scoring for Conversion Funnels

An e-commerce site uses session scoring to identify users who abandon their cart in under three seconds after clicking an ad, flagging them as low-intent or non-human traffic.

// Logic: Score sessions based on interaction with the conversion funnel.
FUNCTION score_session(session):
  IF session.path == "/checkout" AND session.time_on_page < 3 THEN
    user_profile = get_user(session.user_id)
    user_profile.fraud_score += 25
    LOG_EVENT("Checkout_Abandon_Bot_Pattern", session.user_id)
  ENDIF

🐍 Python Code Examples

This Python function simulates detecting abnormally high click frequency from a single IP address. It tracks click timestamps and flags an IP if it exceeds a defined threshold, a common sign of bot activity.

CLICK_LOGS = {}
TIME_WINDOW = 60  # seconds
CLICK_THRESHOLD = 10

def is_click_fraud(ip_address):
    import time
    current_time = time.time()
    
    if ip_address not in CLICK_LOGS:
        CLICK_LOGS[ip_address] = []
    
    # Remove clicks older than the time window
    CLICK_LOGS[ip_address] = [t for t in CLICK_LOGS[ip_address] if current_time - t < TIME_WINDOW]
    
    # Add current click
    CLICK_LOGS[ip_address].append(current_time)
    
    # Check if click count exceeds threshold
    if len(CLICK_LOGS[ip_address]) > CLICK_THRESHOLD:
        print(f"Fraud Detected: IP {ip_address} exceeded click threshold.")
        return True
        
    return False

# Simulation
is_click_fraud("192.168.1.10") # Returns False
for _ in range(12):
    is_click_fraud("192.168.1.10") # Will eventually return True

This example demonstrates filtering traffic based on a user agent string. The function checks if a user agent belongs to a known bot or a non-standard browser, helping to block automated traffic sources.

KNOWN_BOTS = ["Googlebot", "Bingbot", "DataCenterBrowser"]

def filter_suspicious_user_agent(user_agent):
    is_suspicious = any(bot_name in user_agent for bot_name in KNOWN_BOTS)
    
    if is_suspicious:
        print(f"Blocking suspicious user agent: {user_agent}")
        return True
    
    print(f"Allowing user agent: {user_agent}")
    return False

# Simulation
filter_suspicious_user_agent("DataCenterBrowser/1.0") # Returns True
filter_suspicious_user_agent("Mozilla/5.0 (Windows NT 10.0; Win64; x64)") # Returns False

Types of Cost Control

• Reactive IP Blocking

  This method involves identifying and blocking IP addresses after they have already demonstrated fraudulent activity. It is effective for stopping repeat offenders but does not prevent the initial fraudulent clicks.

• Proactive Signature-Based Filtering

  This approach uses a database of known fraudulent signatures, such as device fingerprints or bot user agents, to block traffic before it can click an ad. It is faster but less effective against new or unknown threats.

• Behavioral Analysis

  This type focuses on post-click user behavior, such as mouse movement, session duration, and page interaction. It distinguishes between human and bot-like patterns to score traffic quality but requires more data processing.

• Heuristic Rule-Based Filtering

  This involves setting up custom rules based on logical conditions, such as "block all clicks from outside a target country" or "flag clicks with abnormally high frequency." It offers granular control but requires manual setup and maintenance.

How Cost Optimization Works

Incoming Ad Traffic (Click/Impression)
           │
           ▼
+--------------------------+
│  Data Collection & Pre-  │
│       Processing         │
+--------------------------+
           │
           ▼
+--------------------------+
│ Real-Time Analysis Engine│
│  (IP, Geo, Behavior...)  │
+--------------------------+
           │
           ▼
+--------------------------+
│   Decision & Enforcement │
│  (Valid │ Invalid)      │
+--------------------------+
      /              
     /                
    ▼                  ▼
┌───────┐        ┌──────────┐
│ Allow │        │  Block   │
└───────┘        └──────────┘
                     /
                    /
       ▼            ▼
+--------------------------+
│  Logging & Reporting     │
│  (Feedback Loop)         │
+--------------------------+

🧠 Core Detection Logic

Example 1: IP Filtering

This logic checks the incoming IP address against a known blocklist of malicious sources, such as data centers, proxies, or IPs with a history of fraudulent activity. It’s a fundamental, first-line defense that filters out obvious non-human traffic before more complex analysis is needed.

FUNCTION check_ip(ip_address):
  IF ip_address IN known_bad_ip_list:
    RETURN "invalid"
  ELSE IF get_ip_type(ip_address) == "Data Center":
    RETURN "invalid"
  ELSE:
    RETURN "valid"
  END IF
END FUNCTION

Example 2: Session Heuristics

This logic analyzes the frequency and timing of events within a user session. An abnormally high number of clicks in a very short period from the same user is a strong indicator of an automated script or bot. This method helps catch behavior that a simple IP check might miss.

FUNCTION check_session(user_id, click_timestamp):
  // Get all recent clicks for this user_id
  session_clicks = get_clicks_for_user(user_id, last_60_seconds)

  // Add current click to the list
  session_clicks.append(click_timestamp)

  IF count(session_clicks) > 10:
    // Check time difference between first and last click
    time_delta = last(session_clicks) - first(session_clicks)
    IF time_delta < 30 seconds:
      RETURN "invalid"
    END IF
  END IF

  RETURN "valid"
END FUNCTION

Example 3: Geo Mismatch

This logic compares the geolocation of a user's IP address with other location-based signals, like their browser's timezone or language settings. A significant mismatch, such as an IP from one country and a timezone from another, suggests the use of a VPN or proxy to mask the user's true location, a common tactic in ad fraud.

FUNCTION check_geo_mismatch(ip_address, browser_timezone):
  ip_country = get_country_from_ip(ip_address)
  timezone_country = get_country_from_timezone(browser_timezone)

  // If IP is in USA but timezone is for Vietnam, flag as suspicious
  IF ip_country != timezone_country:
    RETURN "invalid"
  ELSE:
    RETURN "valid"
  END IF
END FUNCTION

📈 Practical Use Cases for Businesses

• Campaign Shielding – Actively blocks clicks from bots and click farms in real time, preventing fraudulent interactions from depleting daily PPC budgets and ensuring ad spend is directed toward genuine prospects.
• Lead Quality Assurance – Filters out fake form submissions and sign-ups generated by automated scripts. This keeps customer relationship management (CRM) systems clean and ensures sales teams spend time on legitimate leads, not phantom contacts.
• ROAS Improvement – By eliminating wasted spend on fraudulent traffic, Cost Optimization directly increases the Return on Ad Spend (ROAS). More of the budget reaches real users, leading to more efficient conversions and a higher overall return.
• Analytics Accuracy – Ensures that marketing analytics and campaign metrics reflect real human behavior. By removing the noise from bots and invalid clicks, businesses can make more accurate, data-driven decisions about strategy and budget allocation.

Example 1: Geofencing Rule

A business running a campaign targeted only to users in Canada can use a geofencing rule to automatically block any clicks originating from IP addresses outside of the target country, saving money and focusing the campaign.

// Rule: Only allow traffic from Canada
FUNCTION check_campaign_geo(user_ip, campaign_target_countries):
  user_country = get_country_from_ip(user_ip)

  IF user_country IN campaign_target_countries:
    // Allow traffic
    RETURN "VALID"
  ELSE:
    // Block traffic
    RETURN "INVALID"
  END IF
END FUNCTION

Example 2: Session Scoring Logic

A system can assign a risk score to each session based on multiple signals. A session with a datacenter IP, a known bot user-agent, and impossibly fast clicking would receive a high-risk score and be blocked before it can waste budget.

FUNCTION calculate_risk_score(session_data):
  risk_score = 0

  IF session_data.ip_type == "Data Center":
    risk_score = risk_score + 40
  END IF

  IF session_data.user_agent CONTAINS "bot":
    risk_score = risk_score + 40
  END IF

  IF session_data.click_frequency > 5 clicks_per_minute:
    risk_score = risk_score + 20
  END IF

  // If score exceeds threshold, block it
  IF risk_score > 75:
    RETURN "BLOCK"
  ELSE:
    RETURN "ALLOW"
  END IF
END FUNCTION

🐍 Python Code Examples

This function simulates checking for abnormally high click frequency from a single source. It keeps a record of click timestamps for each IP and flags an IP as fraudulent if it exceeds a defined threshold within a short time window, a common sign of bot activity.

from collections import defaultdict
from datetime import datetime, timedelta

CLICK_LOGS = defaultdict(list)
TIME_WINDOW = timedelta(seconds=60)
CLICK_THRESHOLD = 15

def is_click_fraud(ip_address):
    """Checks if an IP has an unusually high click frequency."""
    now = datetime.now()
    
    # Remove old timestamps outside the window
    CLICK_LOGS[ip_address] = [t for t in CLICK_LOGS[ip_address] if now - t < TIME_WINDOW]
    
    # Add the new click
    CLICK_LOGS[ip_address].append(now)
    
    # Check if click count exceeds the threshold
    if len(CLICK_LOGS[ip_address]) > CLICK_THRESHOLD:
        print(f"Fraudulent activity detected from IP: {ip_address}")
        return True
        
    return False

# Simulation
is_click_fraud("192.168.1.100") # Returns False
# Simulate 20 rapid clicks
for _ in range(20):
    is_click_fraud("8.8.8.8") # Will eventually return True

This example demonstrates filtering traffic based on the User-Agent string. Many simple bots use generic or known bot-like user agents. This function checks the incoming user agent against a blocklist of suspicious signatures to perform a basic but effective filtering step.

KNOWN_BOT_AGENTS = [
    "crawler",
    "bot",
    "spider",
    "python-requests",
]

def is_suspicious_user_agent(user_agent_string):
    """Filters traffic based on a blocklist of bot-like user agents."""
    ua_lower = user_agent_string.lower()
    for bot_signature in KNOWN_BOT_AGENTS:
        if bot_signature in ua_lower:
            print(f"Suspicious user agent blocked: {user_agent_string}")
            return True
            
    return False

# Simulation
is_suspicious_user_agent("Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36") # False
is_suspicious_user_agent("MyAwesomeBot/1.0 (+http://example.com/bot.html)") # True

Types of Cost Optimization

• Rule-Based Optimization – This type uses predefined filters and rules to block traffic. For example, an advertiser can create a rule to block all clicks from a specific country or from IP addresses known to be data centers. This method is straightforward and effective against known, simple threats.
• Heuristic Optimization – This method analyzes behavior and patterns to identify suspicious activity. It looks for anomalies that deviate from typical human behavior, such as clicking on ads too quickly, having no mouse movement, or visiting pages in a non-sequential pattern.
• Predictive (AI-Based) Optimization – Using machine learning, this type proactively identifies new and evolving threats. The system learns from vast datasets to recognize complex fraud patterns that rules or heuristics might miss, offering a more adaptive and powerful defense against sophisticated bots.
• Hybrid Optimization – This approach combines rule-based, heuristic, and AI-based methods into a single, layered system. By using multiple techniques, it provides the most comprehensive protection, catching a wider range of fraudulent activity from simple bots to advanced, human-like automated attacks.

How Cost Per Action CPA Works

User Click → Ad Server → Landing Page → Action (e.g., Purchase, Signup)
     │                     │                    │
     └─ [Data Capture]      │                    │
          (IP, UA, Time)   │                    │
                           └─ [Pre-Action Analysis]
                                (Session Scoring)
                                          │
                                          └─ [Action Validation] → Is Action Legitimate?
                                                 │
                                     ┌───────────┴───────────┐
                                     │ (Yes)                 │ (No)
                                     ↓                       ↓
                                Mark as Valid        Block & Flag as Fraud

🧠 Core Detection Logic

Example 1: Click-to-Action Time Anomaly

This logic flags conversions that happen too quickly after a click, a common sign of bot automation. It fits within the action validation stage of traffic protection by analyzing the time difference between the initial click and the successful action, filtering out non-human speed.

FUNCTION check_action_time(click_timestamp, action_timestamp):
  time_diff = action_timestamp - click_timestamp
  
  IF time_diff < MIN_THRESHOLD_SECONDS THEN
    RETURN "FRAUDULENT: Action too fast"
  ELSE IF time_diff > MAX_THRESHOLD_SECONDS THEN
    RETURN "SUSPICIOUS: Action took too long"
  ELSE
    RETURN "VALID"
  END IF
END FUNCTION

Example 2: IP and Geolocation Mismatch

This rule checks for inconsistencies between an IP address’s physical location and the location data provided by a user in a form (e.g., shipping address or country registration). It helps detect attempts to bypass geo-targeted campaigns or mask the true origin of fraudulent traffic.

FUNCTION verify_geolocation(user_ip, user_provided_country):
  ip_country = get_country_from_ip(user_ip)

  IF ip_country != user_provided_country THEN
    FLAG "GEO_MISMATCH_FRAUD"
    RETURN FALSE
  END IF

  RETURN TRUE
END FUNCTION

Example 3: Repetitive Action from a Single Source

This logic identifies when multiple distinct actions (e.g., lead submissions with different email addresses) originate from the same IP address or device fingerprint within a short timeframe. It’s effective at catching click farms or bots attempting to generate numerous fake conversions.

FUNCTION check_repetitive_actions(source_ip, time_window):
  action_count = count_actions_from_ip(source_ip, time_window)

  IF action_count > ACTION_LIMIT THEN
    BLOCK_IP(source_ip)
    RETURN "FRAUDULENT_ACTIVITY_DETECTED"
  END IF
  
  RETURN "OK"
END FUNCTION

📈 Practical Use Cases for Businesses

• Campaign Shielding – Protects advertising budgets by automatically filtering out traffic from sources that generate clicks but no valuable actions, ensuring money is spent on potential customers.
• Lead Quality Assurance – Improves lead generation by invalidating form submissions from bots or fraudulent users, ensuring the sales team receives clean, actionable data.
• ROI Optimization – Enhances return on investment by focusing ad spend on channels and audiences that deliver genuine conversions, not just inflated click metrics.
• Analytics Integrity – Ensures marketing analytics reflect true user engagement by scrubbing data of fraudulent interactions, leading to more accurate business intelligence and strategy.

Example 1: Geofencing for Local Services

A local business running a geo-targeted campaign can use CPA data to validate that conversions are coming from within their service area. This logic prevents paying for leads generated by bots using out-of-area proxies.

// Rule: Validate that the action's IP is within the target radius
FUNCTION is_action_in_zone(action_ip, campaign_geo_target):
  action_location = get_location(action_ip)
  
  IF distance_between(action_location, campaign_geo_target.center) <= campaign_geo_target.radius THEN
    RETURN TRUE // Valid Action
  ELSE
    RETURN FALSE // Fraudulent Action (Out of Zone)
  END IF
END FUNCTION

Example 2: Session Scoring for E-commerce

An e-commerce store can score user sessions to identify suspicious purchases. A high score, indicating abnormal behavior (e.g., no mouse movement, instant checkout), flags the transaction for review before fulfillment, preventing chargeback fraud.

// Logic: Score a session based on multiple behavioral factors
FUNCTION calculate_session_score(session_data):
  score = 0
  IF session_data.time_on_page < 5 THEN score += 30
  IF session_data.mouse_events == 0 THEN score += 40
  IF session_data.cart_to_purchase_time < 10 THEN score += 30

  // If score exceeds threshold, flag as high-risk
  IF score > 75 THEN
    FLAG "HIGH_RISK_TRANSACTION"
  END IF
END FUNCTION

🐍 Python Code Examples

This function simulates checking the time between a click and a subsequent action. Actions completed in an impossibly short time are flagged as likely bot activity, which is a common indicator of CPA fraud.

from datetime import datetime, timedelta

def check_conversion_speed(click_time_str, action_time_str, min_seconds=3):
    click_time = datetime.fromisoformat(click_time_str)
    action_time = datetime.fromisoformat(action_time_str)
    
    time_difference = action_time - click_time
    
    if time_difference < timedelta(seconds=min_seconds):
        print(f"Fraud Alert: Action completed in {time_difference.seconds} seconds. Too fast.")
        return False
    
    print("Action speed is within acceptable limits.")
    return True

# Example Usage
check_conversion_speed("2025-07-17T10:00:00", "2025-07-17T10:00:01")

This example demonstrates how to filter incoming actions based on a blocklist of known fraudulent IP addresses. Maintaining such a list is a fundamental technique in protecting campaigns from repeat offenders.

FRAUDULENT_IPS = {"192.168.1.101", "203.0.113.55", "198.51.100.22"}

def filter_action_by_ip(action_ip):
    if action_ip in FRAUDULENT_IPS:
        print(f"Blocking action from known fraudulent IP: {action_ip}")
        return False
    
    print(f"Accepting action from IP: {action_ip}")
    return True

# Example Usage
filter_action_by_ip("203.0.113.55")
filter_action_by_ip("91.108.4.200")

Types of Cost Per Action CPA

• Rule-Based CPA Filtering – This method uses a predefined set of static rules to identify fraud. For example, it might block any action originating from a known data center IP address or if the time from click to action is less than three seconds. It is fast but can be rigid.
• Behavioral CPA Analysis – This type analyzes patterns in user behavior over time to detect anomalies. It looks at session duration, mouse movements, and navigation paths to distinguish between human and bot-like interactions, offering more nuanced detection than static rules.
• Score-Based CPA Validation – This approach assigns a risk score to each action based on multiple factors, such as IP reputation, device fingerprint, and behavioral heuristics. Actions exceeding a certain score are flagged as fraudulent, allowing for a more flexible and accurate assessment.
• Honeypot-Based Detection – In this technique, invisible "honeypot" fields are added to forms. Since real users cannot see these fields, they leave them blank. Bots, however, often fill out all fields automatically. An entry in a honeypot field is a clear indicator of a fraudulent action.

How Cost Per Action CPA fraud Works

+------------------+     +----------------+     +-------------------+     +-------------+     +-------------------+
| Fraudster/Bot    | --> |   Ad Platform  | --> | Advertiser's Site | --> |  Fake Action  | --> | Unearned Payout   |
| (Traffic Source) |     | (Tracking Link)|     | (Landing Page)    |     | (e.g., submit)|     | (to Fraudster)    |
+------------------+     +----------------+     +-------------------+     +-------------+     +-------------------+
          |                      '                      '                       '                     '
          └───────────────────────'──────────────────────'───────────────────────'─────────────────────'
                         Simulates legitimate user journey to trigger a conversion

🧠 Core Detection Logic

Example 1: Action Timing Analysis

This logic flags conversions that happen too quickly or at a machine-like pace. A real user takes time to read a page, fill in form fields, and click submit. A bot can perform these actions in milliseconds. This check fits within a real-time traffic filtering system to analyze the timestamp between a page loading and the conversion event firing.

FUNCTION checkActionTime(session):
  pageLoadTime = session.getPageLoadTimestamp()
  actionTime = session.getActionTimestamp()

  timeToAction = actionTime - pageLoadTime

  IF timeToAction < MIN_THRESHOLD_SECONDS:
    RETURN "FLAG_AS_FRAUD"
  ELSE:
    RETURN "LEGITIMATE"

Example 2: IP and Action Correlation

This logic identifies when multiple, distinct conversion actions originate from a single IP address within a short time frame. It's highly unlikely that many different "real" users would sign up for a service from the same IP address in minutes. This type of analysis is typically run in near-real-time to detect botnet activity.

FUNCTION checkIpFrequency(ip_address, action_type):
  action_log = getActionsByIp(ip_address, last_hour)
  count = 0
  FOR action IN action_log:
    IF action.type == action_type:
      count = count + 1

  IF count > MAX_ACTIONS_PER_HOUR:
    RETURN "BLOCK_IP"
  ELSE:
    RETURN "MONITOR"

Example 3: Honeypot Field Detection

A honeypot involves placing a hidden field in a registration or checkout form that is invisible to human users but not to bots. Bots are programmed to fill every field they find, so if the honeypot field contains data upon submission, the system knows it was filled out by a bot. This is a simple but highly effective real-time detection method.

// HTML part
<form action="/submit" method="post">
  <input type="text" name="real_name">
  <!-- This field is hidden from users via CSS -->
  <input type="text" name="honeypot_field" style="display:none;">
  <button type="submit">Submit</button>
</form>

// Server-side pseudocode
FUNCTION processForm(form_data):
  IF form_data.honeypot_field IS NOT EMPTY:
    // Bot detected
    RETURN "REJECT_SUBMISSION"
  ELSE:
    // Process legitimate submission
    RETURN "ACCEPT_SUBMISSION"

📈 Practical Use Cases for Businesses

• Lead Generation Filtering: Businesses use CPA fraud detection to analyze incoming leads from forms and sign-ups. This ensures that the sales team only engages with genuinely interested prospects, improving efficiency and preventing wasted resources on fake contacts.
• E-commerce Transaction Shielding: Online stores apply these techniques to identify and block fraudulent purchases made with stolen credit cards or by automated bots. This reduces chargebacks, inventory loss, and financial penalties from payment processors.
• Affiliate Program Integrity: Companies with affiliate or publisher programs use CPA fraud analysis to monitor the quality of traffic sent by their partners. It helps in identifying and terminating relationships with affiliates who are driving fake conversions, protecting the integrity of the program.
• Mobile Install Verification: For businesses promoting mobile apps on a Cost-Per-Install basis, fraud detection is used to verify that each installation comes from a real device and user, not from emulators or device farms designed to fake installs.

Example 1: Geolocation Mismatch Rule

This logic checks if the IP address location of the user completing the action matches the location data provided in a form (e.g., city, postal code). A mismatch is a strong indicator of fraud, where bots use proxy IPs that don't align with the fake user data they submit.

FUNCTION verifyGeoLocation(ipAddress, formData):
  ipLocation = getLocationFromIP(ipAddress)
  formLocation = getLocationFromAddress(formData.address)

  IF distance(ipLocation, formLocation) > ACCEPTABLE_RADIUS_KM:
    FLAG "High Risk: Geo Mismatch"
  ELSE:
    FLAG "Low Risk"

Example 2: Session Behavior Scoring

This approach scores a user session based on multiple behavioral data points. Lack of mouse movement, impossibly fast form completion, and standard screen resolutions are all signs of non-human activity. A session that accumulates a high-risk score is blocked from converting.

FUNCTION scoreSession(sessionData):
  riskScore = 0

  IF sessionData.mouseMovements < 10:
    riskScore = riskScore + 30

  IF sessionData.timeOnPage < 5_SECONDS:
    riskScore = riskScore + 40

  IF sessionData.isStandardBotResolution():
    riskScore = riskScore + 20

  IF riskScore > 75:
    RETURN "BLOCK_ACTION"
  ELSE:
    RETURN "ALLOW_ACTION"

🐍 Python Code Examples

This Python function simulates checking for abnormally fast conversions. It calculates the time difference between a page view and a conversion event and flags any action that occurs faster than a defined minimum threshold, which is typical of bot activity.

import datetime

def check_conversion_speed(page_view_time, conversion_time, min_seconds=5):
    """Flags a conversion if it happens too quickly after a page view."""
    time_delta = conversion_time - page_view_time
    if time_delta.total_seconds() < min_seconds:
        print(f"FRAUD ALERT: Conversion completed in {time_delta.total_seconds()} seconds. Likely a bot.")
        return False
    print("Conversion speed is within normal parameters.")
    return True

# Example Usage
page_load = datetime.datetime.now()
# Simulate a bot converting almost instantly
bot_conversion = page_load + datetime.timedelta(seconds=2)
check_conversion_speed(page_load, bot_conversion)

This code example demonstrates how to identify fraudulent activity by checking for an unusually high number of actions from a single IP address. This pattern often indicates a bot or a single user attempting to manipulate CPA campaigns.

def analyze_ip_activity(event_logs, ip_address, time_window_minutes=60, max_actions=5):
    """Analyzes logs to detect too many actions from a single IP in a given time window."""
    from collections import defaultdict
    
    ip_actions = defaultdict(int)
    for log in event_logs:
        if log['ip'] == ip_address:
            ip_actions[log['action_type']] += 1

    for action, count in ip_actions.items():
        if count > max_actions:
            print(f"FRAUD ALERT: IP {ip_address} performed action '{action}' {count} times. Flagged for review.")
            return True
    print(f"IP {ip_address} activity is normal.")
    return False

# Example Usage
logs = [
    {'ip': '203.0.113.1', 'action_type': 'signup'},
    {'ip': '203.0.113.1', 'action_type': 'signup'},
    {'ip': '203.0.113.1', 'action_type': 'signup'},
    {'ip': '203.0.113.1', 'action_type': 'signup'},
    {'ip': '203.0.113.1', 'action_type': 'signup'},
    {'ip': '203.0.113.1', 'action_type': 'signup'},
]
analyze_ip_activity(logs, '203.0.113.1')

Types of Cost Per Action CPA fraud

• Lead Generation Fraud: In this type, fraudsters use bots or human farms to submit fake information into lead forms, such as contact requests or newsletter sign-ups. Advertisers pay for these worthless leads, which clog sales funnels and waste resources.
• App Install Fraud: Common in mobile advertising, this involves faking app installations to claim a payout. Fraudsters use device farms or emulators to generate thousands of installs that have no real user engagement, draining marketing budgets for user acquisition campaigns.
• E-commerce and Sales Fraud: Fraudsters automate the process of making purchases, often using stolen credit card numbers. While the initial "sale" is registered and the affiliate is paid, the transaction eventually results in a chargeback, causing the advertiser to lose both the product and the commission.
• Cookie Stuffing: This method involves surreptitiously dropping multiple affiliate tracking cookies onto a user's browser without their knowledge. If the user later completes a purchase or action on one of those sites organically, the fraudster illegitimately receives the CPA commission.
• Incentivized Action Abuse: This occurs when publishers offer users micro-rewards to complete a specific action, such as signing up for a trial. These users have no genuine interest in the product and are only there for the reward, leading to low-quality conversions that do not translate into long-term value.

How Cost Per Click CPC Works

+---------------------+      +----------------------+      +-------------------------+      +------------------+
|    1. User Click    | →    |  2. Ad Platform Logs | →    | 3. Fraud Detection Scan | →    |  4. Traffic Score|
+---------------------+      +----------------------+      +-------------------------+      +------------------+
          │                                                          │                           │
          │                                                          │                           └─+ Valid Click
          └──────────────────────────────────────────────────────────┘                             | (Charge Advertiser)
                                                                                                   │
                                                                                                   └─+ Invalid Click
                                                                                                     (Block & Report)

🧠 Core Detection Logic

Example 1: IP Frequency Capping

This logic prevents a single source from depleting an ad budget through repeated clicks. It works by setting a threshold for the number of clicks allowed from one IP address within a specific timeframe. It’s a foundational layer of defense against basic bots and manual click fraud.

FUNCTION analyze_click(click_data):
  ip = click_data.ip_address
  timestamp = click_data.timestamp
  
  // Define rule: 3 clicks max from one IP in 24 hours
  THRESHOLD_COUNT = 3
  TIMEFRAME_HOURS = 24

  recent_clicks = get_clicks_from_ip_in_last(ip, TIMEFRAME_HOURS)

  IF count(recent_clicks) >= THRESHOLD_COUNT:
    RETURN "FRAUDULENT: IP click frequency exceeded"
  ELSE:
    RETURN "VALID"

Example 2: Geographic Mismatch Detection

This rule identifies fraud when a click’s purported location doesn’t align with other data signals. For instance, if an IP address is registered in one country but the browser’s language setting or timezone indicates another, it could signal the use of a proxy or VPN to disguise the user’s true origin.

FUNCTION analyze_geolocation(click_data):
  ip_location = get_location_from_ip(click_data.ip_address)
  browser_timezone = click_data.user_agent.timezone
  campaign_target_geo = "USA"

  // Rule: Block if IP is outside the campaign's target geography
  IF ip_location.country != campaign_target_geo:
    RETURN "FRAUDULENT: Click outside of target geography"
  
  // Rule: Flag if browser timezone is inconsistent with IP location
  IF not is_timezone_consistent(browser_timezone, ip_location.country):
    RETURN "SUSPICIOUS: Timezone and IP location mismatch"
  
  RETURN "VALID"

Example 3: Session Behavior Analysis

This logic assesses the quality of a click by analyzing post-click engagement. A genuine user is expected to interact with the landing page, whereas a bot often leaves immediately. Very short session durations with no interaction are a strong indicator of low-quality or fraudulent traffic.

FUNCTION analyze_session(session_data):
  duration = session_data.time_on_page_seconds
  scroll_depth_percent = session_data.scroll_depth
  events_fired = count(session_data.interaction_events)

  // Rule: A session under 2 seconds with no interaction is invalid
  IF duration < 2 AND scroll_depth_percent == 0 AND events_fired == 0:
    RETURN "FRAUDULENT: Zero engagement bounce"
  
  // Rule: A session with some interaction is likely valid
  IF duration > 10 OR scroll_depth_percent > 20:
    RETURN "VALID"
    
  RETURN "SUSPICIOUS"

📈 Practical Use Cases for Businesses

• Campaign Shielding – Automatically block clicks from known fraudulent sources like data centers and competitor IP addresses, ensuring that the ad budget is spent on reaching genuine potential customers.
• Data Integrity – By filtering out bot traffic, businesses ensure their analytics (like click-through rate and conversion rate) are accurate, leading to better-informed marketing decisions and strategy adjustments.
• ROI Optimization – Preventing budget waste from fake clicks directly improves Return on Ad Spend (ROAS). More of the budget is spent on clicks that have a real chance of converting into sales.
• Competitor Attack Mitigation – Detect and block malicious clicking activity from competitors who aim to exhaust your daily ad budget and remove your ads from appearing in search results.
• Lead Generation Filtering – Ensure that form submissions and leads generated from PPC campaigns are from actual interested humans, not bots, improving the quality of the sales pipeline and saving follow-up time.

Example 1: Geofencing Rule

A local service business that only operates in California can use geofencing to automatically block any clicks originating from IP addresses outside the United States, protecting its budget from irrelevant international traffic.

// Business Rule: Only allow clicks from the United States
FUNCTION apply_geofencing(click_data):
  allowed_countries = ["US"]
  click_country = get_country_from_ip(click_data.ip_address)

  IF click_country IN allowed_countries:
    // Further check for state-level targeting if needed
    RETURN "ALLOW"
  ELSE:
    // Block the click and add IP to temporary exclusion list
    block_ip(click_data.ip_address)
    RETURN "BLOCK"

Example 2: Session Quality Scoring

An e-commerce store can implement a session scoring system. Clicks that result in a session shorter than three seconds with no scrolling are flagged as low-quality. If an IP generates multiple low-quality scores, it’s automatically blocked.

// Business Rule: Score traffic based on engagement
FUNCTION score_session_quality(session):
  score = 0
  IF session.duration_seconds > 5:
    score += 1
  IF session.scroll_percentage > 30:
    score += 1
  IF session.form_interactions > 0:
    score += 2
  
  // A score less than 1 is considered low quality
  IF score < 1:
    flag_source_as_low_quality(session.source_ip)
  
  RETURN score

🐍 Python Code Examples

This Python function simulates checking a click's IP address against a predefined blocklist. This is a simple but effective first line of defense in a traffic protection system to filter out known malicious actors.

# A set of known fraudulent IP addresses
FRAUDULENT_IPS = {"192.168.1.101", "203.0.113.55", "198.51.100.22"}

def is_ip_blocked(ip_address):
  """Checks if a given IP address is in the fraudulent IP set."""
  if ip_address in FRAUDULENT_IPS:
    print(f"Blocking known fraudulent IP: {ip_address}")
    return True
  return False

# Example usage:
click_ip = "203.0.113.55"
is_ip_blocked(click_ip)

This code analyzes click timestamps from a single user to detect abnormally high frequency. Real users don't click ads every few seconds, so this logic helps identify automated bots designed to generate a high volume of fraudulent clicks quickly.

from datetime import datetime, timedelta

def has_abnormal_frequency(clicks, time_window_seconds=60, max_clicks=5):
  """Analyzes click timestamps to detect suspiciously high frequency."""
  if len(clicks) < max_clicks:
    return False

  # Sort clicks by time to be safe
  clicks.sort(key=lambda x: x['timestamp'])
  
  time_difference = clicks[-1]['timestamp'] - clicks['timestamp']
  
  if time_difference < timedelta(seconds=time_window_seconds):
    print(f"Fraud Alert: {len(clicks)} clicks detected in under {time_window_seconds} seconds.")
    return True
  return False

# Example usage with simulated click data:
user_clicks = [
    {'timestamp': datetime.now() - timedelta(seconds=10)},
    {'timestamp': datetime.now() - timedelta(seconds=8)},
    {'timestamp': datetime.now() - timedelta(seconds=5)},
    {'timestamp': datetime.now() - timedelta(seconds=4)},
    {'timestamp': datetime.now() - timedelta(seconds=2)},
    {'timestamp': datetime.now()}
]
has_abnormal_frequency(user_clicks)

Types of Cost Per Click CPC

• Manual CPC – Advertisers set a maximum bid for their ads. In fraud detection, sudden spikes in clicks at the maximum bid can be a red flag, as bots often click aggressively without regard for the auction dynamics that typically lower the actual CPC.
• Enhanced CPC (eCPC) – An automated bidding strategy where the ad platform adjusts manual bids up or down based on the likelihood of a conversion. Fraudulent clicks with no conversion history can trick the system into lowering bids, but analysis can reveal sources that consistently fail to convert.
• Rule-Based CPC Filtering – Not a bidding type, but a protection method where CPC data is analyzed against predefined rules. For example, a rule might flag any source that generates clicks costing more than $100 in an hour without any corresponding user engagement or conversions.
• CPC Anomaly Detection – This approach uses machine learning to establish a baseline for normal CPC values and click patterns for a campaign. It then automatically flags significant deviations, such as a sudden, unexplained drop or spike in average CPC, which could indicate a new fraud attack.

How Cost per completed view CPCV Works

Ad Request → Ad Serve → [Viewability & Engagement Tracking] → Completion Event → Validation → Billable View
      │                      │                                     │
      └──────────────────────┼─────────────────────────────────────┘
                             ↓
                       [Fraud Filter] → (Discarded: Bot traffic, non-viewable, incomplete)

🧠 Core Detection Logic

Example 1: View Duration and Completion Mismatch

This logic flags views where the reported “completed view” event happens in a timeframe that is impossibly short for the video’s actual length. It’s effective at catching bots or compromised ad players that fire completion pixels without actually playing the ad.

// Define video properties
VIDEO_DURATION_SECONDS = 30;
MIN_REASONABLE_VIEW_TIME_SECONDS = 29;

// On receiving a completion event
function validateCompletion(event) {
  let viewTime = event.actual_view_duration;
  let isComplete = event.is_complete_flag;

  if (isComplete && viewTime < MIN_REASONABLE_VIEW_TIME_SECONDS) {
    flagAsFraud(event.session_id, "Completion time anomaly");
  } else {
    countAsValidView(event.session_id);
  }
}

Example 2: Session-Based Completion Rate Heuristics

This approach analyzes the behavior of a single user (or IP address) across a session. A user who completes 100% of video ads viewed within a short period is highly suspicious, as real users often skip ads or navigate away. This helps identify non-human, goal-oriented traffic.

// Session tracking object
SESSION_DATA = {
  "user_123": {
    views: 50,
    completions: 50,
    session_duration_minutes: 10
  }
};

// Analysis logic
function checkSession(session) {
  let completionRate = session.completions / session.views;
  
  if (session.views > 20 && completionRate >= 0.98) {
    flagAsSuspicious(session, "Unnaturally high completion rate");
  }
}

Example 3: Viewability Pre-Conditioning

This rule ensures that a completion event is only considered valid if the ad also met viewability standards for a significant portion of its duration. It prevents paying for completed views that occurred in a background tab or were otherwise not visible to the user, a common issue with impression fraud.

// Viewability data received from measurement script
VIEW_METRICS = {
  viewable_percentage: 100, // % of ad pixels in view
  viewable_duration_seconds: 30
};

// Validation logic
function verifyViewabilityOnCompletion(event, viewMetrics) {
  let isComplete = event.is_complete_flag;

  // IAB standard: 50% of pixels in view for at least 2 consecutive seconds
  let isViewable = (viewMetrics.viewable_percentage >= 50 && viewMetrics.viewable_duration_seconds >= 2);

  if (isComplete && !isViewable) {
    discardView(event.session_id, "View completed but was not viewable");
  } else {
    countAsValidView(event.session_id);
  }
}

📈 Practical Use Cases for Businesses

Businesses use the Cost per completed view (CPCV) model to directly tie ad spend to tangible engagement, which provides a layer of protection against fraudulent and low-quality traffic. By paying only for fully watched ads, companies ensure their budget is spent on an audience that has received the entire brand message.

• Budget Protection: CPCV ensures that advertising funds are not wasted on unviewed or fraudulent impressions, directly linking cost to verified engagement and maximizing return on ad spend (ROAS).
• Creative Performance Analysis: By analyzing completion rates, businesses can gain clear insights into how compelling their video creative is, allowing them to optimize content for better audience retention.
• Publisher Quality Assessment: A low completion rate from a specific publisher or app can signal either a poor user experience or a source of low-quality, invalid traffic, helping advertisers refine their media buying strategies.
• High-Impact Brand Campaigns: For campaigns focused on storytelling and brand building, CPCV guarantees that the full narrative is delivered to the viewer, which is essential for message retention and brand recall.

Example 1: Filtering Non-Human Traffic from Data Centers

This pseudocode logic checks the IP address of a completed view against a known list of data center IP ranges. Since real users do not browse from servers, this rule blocks a common source of bot-generated views.

// List of known data center IP ranges
DATA_CENTER_IPS = ["198.51.100.0/24", "203.0.113.0/24"];

// Function to check IP on completion
function handleViewCompletion(viewEvent) {
  let userIP = viewEvent.ip_address;
  
  if (isWithinRanges(userIP, DATA_CENTER_IPS)) {
    blockAndLog(userIP, "Data Center Traffic");
  } else {
    // Proceed to count as valid view
    recordValidCompletion(viewEvent);
  }
}

Example 2: Session Velocity Scoring

This logic scores a user session based on the number of completed views within a given timeframe. An abnormally high velocity (too many completions too quickly) suggests automation and leads to the traffic being flagged as suspicious.

// Session tracking
session = {
  user_id: "user_abc",
  start_time: "2024-10-26T10:00:00Z",
  completed_views: 0
};
TIME_WINDOW_SECONDS = 3600; // 1 hour
VELOCITY_THRESHOLD = 50; // Max 50 completed views per hour

// Increment on each completed view
session.completed_views++;

// Check velocity
let timeElapsed = now() - session.start_time;
if (timeElapsed < TIME_WINDOW_SECONDS && session.completed_views > VELOCITY_THRESHOLD) {
  flagSessionForReview(session.user_id, "High completion velocity");
}

🐍 Python Code Examples

This Python function simulates checking for unnaturally fast completion times. It helps detect bots or manipulated clients that fire a completion event without playing the video for its full duration.

def check_completion_time_anomaly(view_event, video_duration=30):
    """Flags a view if its completion time is suspiciously short."""
    view_duration = view_event.get("view_duration_seconds", 0)
    is_complete = view_event.get("is_complete", False)
    
    # Allow for a small buffer (e.g., 1 second)
    min_required_time = video_duration - 1
    
    if is_complete and view_duration < min_required_time:
        print(f"FRAUD DETECTED: View from IP {view_event['ip']} completed in {view_duration}s. Anomaly.")
        return False
    return True

# Example Usage
view = {"ip": "203.0.113.10", "is_complete": True, "view_duration_seconds": 3}
check_completion_time_anomaly(view)

This script processes a list of view logs to identify IP addresses that exhibit an unusually high frequency of completed views in a short time, a common pattern for bot farms.

from collections import Counter

def find_high_frequency_ips(view_logs, time_limit_minutes=60, frequency_threshold=100):
    """Identifies IPs with an excessive number of completed views."""
    ip_counts = Counter(log['ip'] for log in view_logs if log['is_complete'])
    
    suspicious_ips = []
    for ip, count in ip_counts.items():
        if count > frequency_threshold:
            print(f"SUSPICIOUS IP: {ip} had {count} completed views.")
            suspicious_ips.append(ip)
            
    return suspicious_ips

# Example Usage
logs = [
    {"ip": "198.51.100.5", "is_complete": True} for _ in range(150)
] + [
    {"ip": "192.168.1.1", "is_complete": True} for _ in range(10)
]
find_high_frequency_ips(logs)

Types of Cost per completed view CPCV

• Standard CPCV: This is the most common type, where an advertiser pays once a video ad has been watched to its conclusion. The definition of "complete" can vary by platform (e.g., 100% of the duration or after 30 seconds).
• Viewable CPCV (vCPCV): A stricter variant where the advertiser only pays if the video was viewed to completion AND met industry viewability standards (e.g., at least 50% of the player's pixels were on screen). This type adds a crucial layer of defense against impression fraud.
• Audible and Viewable CPCV (AV-CPCV): This is one of the highest-quality standards. It requires the video to be completed, viewable, and have the sound on. This helps filter out muted autoplay videos and ensures the user was more likely to be actively engaged.
• User-Initiated CPCV: Payment is triggered for a completed view only if the user actively chose to play the video (e.g., clicked a play button). This helps differentiate between passive, autoplay views and active, intentional viewership, which is considered more valuable.
• Third-Party Verified CPCV: In this model, a neutral, independent measurement company is used to validate all completed views. This removes potential reporting conflicts of interest from publishers and provides advertisers with trusted, fraud-free metrics.

How Cost per engagement Works

Incoming Ad Traffic -> +--------------------------+ -> [Legitimate User] -> Serve Ad
                         | Engagement Analysis System |
                         +--------------------------+ -> [Fraudulent Bot]  -> Block & Log
                                      |
                                      └─ Behavioral Data (Clicks, Mouse Moves, Timestamps)

🧠 Core Detection Logic

Example 1: Session Engagement Scoring

This logic scores a user session in real-time based on a collection of engagement metrics. It’s used to differentiate between a curious human and a simple bot by quantifying the quality of interaction. A low cumulative score indicates non-human behavior, leading to the traffic being flagged.

function calculateEngagementScore(session) {
  let score = 0;
  // Award points for human-like mouse movement
  if (session.mouseMovements > 10) score += 20;
  // Award points for realistic time on page (e.g., > 3 seconds)
  if (session.timeOnPage > 3000) score += 30;
  // Award points for scrolling
  if (session.scrollDepth > 25) score += 25;
  // Penalize for known bot markers
  if (session.isFromDataCenterIP) score -= 50;

  return score;
}

// Decision Logic
let userSession = {mouseMovements: 15, timeOnPage: 5000, scrollDepth: 40, isFromDataCenterIP: false};
let engagementScore = calculateEngagementScore(userSession);

if (engagementScore < 50) {
  blockRequest("Low Engagement Score");
} else {
  serveAd();
}

Example 2: Engagement Rate Anomaly Detection

This logic is used to identify coordinated bot attacks by monitoring the engagement rate from a specific source, such as an IP address or subnet. An abnormally high number of engagements in a short period is a strong indicator of automated, non-human activity.

// Monitor engagements per IP address
const engagementLog = {};
const ENGAGEMENT_THRESHOLD = 10; // Max 10 engagements per minute
const TIME_WINDOW = 60000; // 1 minute in milliseconds

function processEngagement(ip) {
  const now = Date.now();
  
  // Initialize IP if not seen before
  if (!engagementLog[ip]) {
    engagementLog[ip] = [];
  }

  // Add current engagement timestamp
  engagementLog[ip].push(now);

  // Filter out old timestamps
  engagementLog[ip] = engagementLog[ip].filter(timestamp => now - timestamp < TIME_WINDOW);

  // Check if threshold is exceeded
  if (engagementLog[ip].length > ENGAGEMENT_THRESHOLD) {
    blockRequest(`High engagement rate from IP: ${ip}`);
    return false;
  }
  
  return true;
}

Example 3: Behavioral Fingerprint Mismatch

This logic checks for inconsistencies between a user's declared device or browser (User-Agent) and their actual behavior. It is effective at catching bots that try to mask their identity by spoofing a legitimate User-Agent string but fail to replicate the corresponding behavior.

function checkBehavioralMismatch(session) {
  const userAgent = session.userAgent;
  const events = session.interactionEvents;

  // Example: A 'mobile' user agent should not have mouse hover events
  if (userAgent.includes("iPhone") && events.includes("mouseHover")) {
    blockRequest("Behavioral mismatch: Mouse events from a mobile device.");
    return;
  }

  // Example: Unnaturally fast clicks are not human
  if (session.clickInterval < 50) { // Clicks less than 50ms apart
    blockRequest("Behavioral mismatch: Click speed is too fast.");
    return;
  }
  
  serveAd();
}

📈 Practical Use Cases for Businesses

• Budget Protection – Prevents ad spend from being wasted on fraudulent clicks and fake engagements generated by bots, ensuring that marketing funds are spent on reaching real potential customers.
• Analytics Integrity – Ensures marketing data is clean and reliable by filtering out invalid traffic. This allows businesses to make accurate decisions based on how real users are interacting with their campaigns.
• Lead Quality Enhancement – By analyzing engagement quality leading up to a form submission, businesses can filter out fake or low-quality leads generated by automated scripts, improving sales efficiency.
• Return on Ad Spend (ROAS) Improvement – Increases ROAS by concentrating ad spend on genuine users who are more likely to convert, thereby maximizing the return from advertising investments.

Example 1: E-commerce Ad Spend Protection

An e-commerce store can use engagement analysis to block bots from clicking on its PPC ads. The following logic scores traffic and blocks IPs with characteristics that are inconsistent with genuine shoppers.

function protectEcommSpend(traffic) {
  let score = 100;

  // Penalize traffic from known hosting providers
  if (isDataCenter(traffic.ip)) {
    score -= 50;
  }
  // Penalize sessions with no mouse movement before a click
  if (traffic.clicks > 0 && traffic.mouseMovements == 0) {
    score -= 40;
  }
  // Penalize suspiciously fast time-to-click
  if (traffic.timeToFirstClick < 1000) { // less than 1 second
    score -= 30;
  }

  if (score < 50) {
    addIpToBlocklist(traffic.ip);
    console.log(`Action: Blocked IP ${traffic.ip} for suspicious engagement.`);
  }
}

Example 2: Lead Generation Form Shielding

A B2B company can use engagement rules to prevent bots from submitting fake leads through its "Contact Us" form. This logic checks for human-like interaction before the form is submitted.

function validateLeadGen(formSubmission) {
  // Rule 1: Ensure a minimum time has passed on the page
  if (formSubmission.timeOnPage < 5000) { // Less than 5 seconds
    return { valid: false, reason: "Form filled too quickly" };
  }
  
  // Rule 2: Ensure there was some interaction with the page
  if (formSubmission.scrollEvents === 0 && formSubmission.mouseMovements < 10) {
    return { valid: false, reason: "Lack of page interaction" };
  }
  
  // Rule 3: Check for hidden honeypot field completion
  if (formSubmission.honeypotField !== "") {
    return { valid: false, reason: "Honeypot field triggered" };
  }

  return { valid: true };
}

🐍 Python Code Examples

This Python function simulates checking for abnormally high click frequency from a single IP address. It helps identify automated click bots by flagging sources that exceed a realistic human clicking rate within a defined time window.

from collections import defaultdict
import time

CLICK_LOGS = defaultdict(list)
TIME_WINDOW_SECONDS = 60
CLICK_THRESHOLD = 15

def is_click_frequency_abnormal(ip_address):
    """Checks if an IP has an abnormal click frequency."""
    current_time = time.time()
    
    # Add new click timestamp
    CLICK_LOGS[ip_address].append(current_time)
    
    # Remove clicks outside the time window
    CLICK_LOGS[ip_address] = [t for t in CLICK_LOGS[ip_address] if current_time - t < TIME_WINDOW_SECONDS]
    
    # Check if click count exceeds threshold
    if len(CLICK_LOGS[ip_address]) > CLICK_THRESHOLD:
        print(f"ALERT: High frequency clicks from {ip_address}")
        return True
        
    return False

# Simulation
is_click_frequency_abnormal("192.168.1.10") # Returns False
# Simulate a burst of clicks
for _ in range(20):
    is_click_frequency_abnormal("192.168.1.25")

This code provides a simple function to score the authenticity of a user session based on behavioral data. It assigns a score based on metrics like mouse activity and time spent on a page, which helps in differentiating legitimate user engagement from suspicious bot activity.

def score_session_authenticity(session_data):
    """Scores a session based on engagement metrics to detect bots."""
    score = 0
    
    # Check for basic human-like behavior
    if session_data.get("time_on_page_secs", 0) > 5:
        score += 40
    if session_data.get("mouse_movements", 0) > 20:
        score += 30
    if session_data.get("scroll_depth_percent", 0) > 30:
        score += 30

    # Penalize for bot-like characteristics
    if session_data.get("is_proxy", False):
        score -= 50
    if session_data.get("user_agent", "").lower().find("bot") != -1:
        score -= 100
        
    is_human = score >= 50
    print(f"Session from IP {session_data.get('ip')} scored {score}. Is human: {is_human}")
    return is_human

# Example sessions
human_session = {"ip": "8.8.8.8", "time_on_page_secs": 35, "mouse_movements": 150, "scroll_depth_percent": 70}
bot_session = {"ip": "1.2.3.4", "time_on_page_secs": 1, "mouse_movements": 0, "scroll_depth_percent": 0, "is_proxy": True}

score_session_authenticity(human_session)
score_session_authenticity(bot_session)

Types of Cost per engagement

• Micro-Engagement Analysis

  This type focuses on subtle, low-level user interactions like mouse movements, hovers, and short interaction times. It is used to detect sophisticated bots that can mimic basic clicks but fail to replicate the nuanced, sub-conscious behaviors of a real human user navigating a page.

• Meaningful Action Validation

  This approach moves beyond simple clicks to validate more significant engagements, such as video views (e.g., watched for 15+ seconds), form fills, or downloads. It helps verify that the engagement is not just a superficial interaction but a genuine signal of user interest and intent.

• Session Velocity Scoring

  This method analyzes the speed and timing of events within a single user session. It flags traffic as fraudulent if interactions occur at an impossible speed, such as clicking on multiple elements simultaneously or completing a form in under a second, which is indicative of automated scripts.

• Funnel Progression Analysis

  This type assesses whether a user continues to engage beyond the initial click. It tracks if the user navigates to other pages, adds an item to a cart, or completes a conversion. A high drop-off rate immediately after the paid engagement is a strong indicator of low-quality or fraudulent traffic.

• Behavioral Consistency Check

  This technique verifies that a user's engagement patterns are consistent with their device fingerprint and browsing history. For example, it would flag a "mobile user" exhibiting desktop-only behaviors or an IP address that rapidly switches between hundreds of different device profiles, suggesting an emulation-based bot.

How Cost per install Works

+----------------+      +-----------------+      +-----------------+      +--------------------+
|   User Click   | →    |   Ad Network    | →    |   App Install   | →    |  Post-Install Data |
+----------------+      +-----------------+      +-----------------+      +--------------------+
                                                        │
                                                        │
                                                        ↓
                                                +----------------+
                                                |  Fraud Check   |
                                                | (CPI Analysis) |
                                                +----------------+
                                                        │
                                          ┌─────────────┴─────────────┐
                                          │                           │
                                +-----------------+         +-------------------+
                                |  Valid Install  |         | Invalid/Fraudulent|
                                +-----------------+         +-------------------+

🧠 Core Detection Logic

Example 1: CPI Anomaly Detection

This logic identifies ad campaigns or publishers with a Cost Per Install that deviates significantly from the expected average. An abnormally low CPI can indicate that a source is generating a high volume of cheap, fraudulent installs using bots, while a sudden spike might suggest a targeted attack. This fits into traffic protection by automatically flagging suspicious channels for review or blocking.

FUNCTION check_cpi_anomaly(campaign_data, avg_cpi_benchmark, tolerance_percent):
  campaign_cpi = campaign_data.total_spend / campaign_data.total_installs
  
  lower_bound = avg_cpi_benchmark * (1 - tolerance_percent)
  upper_bound = avg_cpi_benchmark * (1 + tolerance_percent)

  IF campaign_cpi < lower_bound OR campaign_cpi > upper_bound:
    RETURN "Anomaly Detected: CPI is outside the expected range."
  ELSE:
    RETURN "CPI is within normal parameters."

Example 2: Click-to-Install Time (CTIT) Heuristics

This logic analyzes the time elapsed between an ad click and the subsequent app install. Fraudulent installs generated by bots or click injection methods often occur almost instantaneously—a behavior rarely seen from genuine users. By setting a minimum time threshold, this rule helps filter out automated fraud at the install validation stage.

FUNCTION validate_install_time(click_timestamp, install_timestamp):
  MIN_CTIT_SECONDS = 10  // Set a realistic minimum time for a user to install

  time_difference = install_timestamp - click_timestamp
  
  IF time_difference < MIN_CTIT_SECONDS:
    RETURN "Fraud Alert: Install time is suspiciously short."
  ELSE:
    RETURN "Install time is valid."

Example 3: Geolocation Mismatch

This logic compares the geographic location derived from the user's IP address at the time of the click with the country reported by the app store upon install. A mismatch often indicates the use of proxies or VPNs to disguise the traffic's true origin, a common tactic in organized install fraud schemes. This check is crucial for ensuring campaign geo-targeting integrity.

FUNCTION check_geo_mismatch(click_ip_location, app_store_country):
  IF click_ip_location.country != app_store_country:
    RETURN "Fraud Warning: Click origin does not match install country."
  ELSE:
    RETURN "Geolocation is consistent."

📈 Practical Use Cases for Businesses

• Campaign Shielding – Protects active advertising campaigns by using CPI data to automatically identify and block traffic sources that deliver high volumes of low-quality or fraudulent installs, preserving the budget for legitimate channels.
• Ad Network Auditing – Allows businesses to vet and continuously evaluate ad network partners by comparing their average CPI and post-install engagement metrics against benchmarks, ensuring they are paying for valuable users, not just installs.
• ROAS Optimization – Improves Return on Ad Spend (ROAS) by cleaning acquisition data. By filtering out fraudulent installs that generate no revenue, businesses get a clearer picture of which campaigns truly drive profitable user growth.
• User Quality Assessment – Helps differentiate between high-value users and low-quality, fraudulent ones. A channel with a realistic CPI and high user engagement is prioritized over one with a suspiciously low CPI and no post-install activity.

Example 1: Ad Network Vetting Rule

This logic helps a business decide whether to continue working with a specific ad network by analyzing the quality of installs provided.

FUNCTION vet_ad_network(network_data, cpi_threshold, engagement_rate_threshold):
  avg_cpi = network_data.total_spend / network_data.installs
  engagement_rate = network_data.engaged_users / network_data.installs
  
  IF avg_cpi < cpi_threshold AND engagement_rate < engagement_rate_threshold:
    // CPI is too low and users aren't engaging, indicating low quality
    PAUSE_CAMPAIGN(network_data.id)
    RETURN "Network flagged for providing low-quality, potentially fraudulent installs."
  ELSE:
    RETURN "Network performance is within acceptable parameters."

Example 2: Real-time Install Validation

This logic provides an immediate check on each incoming install to discard obvious fraud before it contaminates analytics dashboards or gets attributed.

FUNCTION validate_real_time_install(install_event):
  // Check 1: Is the install from a known fraudulent IP (datacenter/proxy)?
  IF is_blacklisted_ip(install_event.ip_address):
    REJECT_INSTALL(install_event.id, reason="Blacklisted IP")
    RETURN
  
  // Check 2: Is the time from click to install impossibly short?
  IF (install_event.timestamp - install_event.click_timestamp) < 5_SECONDS:
    REJECT_INSTALL(install_event.id, reason="Click-to-Install time too short")
    RETURN
    
  ACCEPT_INSTALL(install_event.id)

🐍 Python Code Examples

This Python function simulates checking for CPI anomalies in campaign data. It identifies campaigns where the Cost Per Install is unusually low compared to a predefined threshold, which can be an indicator of large-scale, low-quality install fraud.

def find_cpi_outliers(campaigns, minimum_cpi_threshold=0.10):
    """Flags campaigns with suspiciously low CPI."""
    fraudulent_campaigns = []
    for campaign_id, data in campaigns.items():
        try:
            cpi = data['spend'] / data['installs']
            if cpi < minimum_cpi_threshold:
                print(f"Flagged Campaign {campaign_id}: CPI of ${cpi:.2f} is below threshold.")
                fraudulent_campaigns.append(campaign_id)
        except ZeroDivisionError:
            print(f"Warning: Campaign {campaign_id} has zero installs.")
    return fraudulent_campaigns

# Example Usage
campaign_data = {
    "A": {"spend": 1000, "installs": 500},  # CPI = $2.00
    "B": {"spend": 50, "installs": 1000},   # CPI = $0.05 (Suspicious)
    "C": {"spend": 1200, "installs": 650}   # CPI = $1.85
}
find_cpi_outliers(campaign_data)

This code analyzes the time difference between a click and an install for a list of events. It helps detect click injection or bot activity where the installation happens too quickly to be humanly possible, a common signature of automated mobile ad fraud.

import datetime

def analyze_click_to_install_time(install_events, min_seconds=10):
    """Analyzes a list of install events for suspicious timing."""
    suspicious_installs = []
    for event in install_events:
        click_time = datetime.datetime.fromisoformat(event['click_ts'])
        install_time = datetime.datetime.fromisoformat(event['install_ts'])
        
        time_delta = (install_time - click_time).total_seconds()
        
        if time_delta < min_seconds:
            print(f"Suspicious Install ID {event['id']}: Time delta was {time_delta}s.")
            suspicious_installs.append(event['id'])
    return suspicious_installs

# Example Usage
events = [
    {"id": 1, "click_ts": "2025-07-17T10:00:00", "install_ts": "2025-07-17T10:02:30"},
    {"id": 2, "click_ts": "2025-07-17T10:01:00", "install_ts": "2025-07-17T10:01:03"}, # Suspicious
    {"id": 3, "click_ts": "2025-07-17T10:03:00", "install_ts": "2025-07-17T10:05:00"}
]
analyze_click_to_install_time(events)

Types of Cost per install

• Standard CPI Analysis – This involves monitoring the direct cost for each app install from a paid ad campaign. In fraud detection, consistently low CPIs from a source are a red flag for high volumes of worthless, bot-generated installs used to drain advertising budgets.
• Effective CPI (eCPI) Analysis – This metric blends costs over both paid and organic installs to provide a holistic view. Fraud protection systems analyze eCPI to detect attribution fraud like click hijacking, where fraudulent clicks steal credit for organic installs, artificially inflating a paid campaign's performance.
• Cost Per Action (CPA) Validation – A more fraud-resistant model where payment is triggered by a specific post-install action (e.g., registration, reaching level 5). Security systems use this to confirm user quality, as bots can fake installs but struggle to mimic complex in-app engagement.
• Geographic CPI Benchmarking – This involves comparing the CPI from different countries against established norms. Fraudsters often use proxies or VPNs to generate installs from high-payout regions, and significant deviations from geographic CPI benchmarks can instantly signal this type of fraudulent activity.