Archives: Glossary Terms

How Brand Protection Works

Incoming Ad Traffic -> +--------------------------+ -> [VALID TRAFFIC] -> Website/App
                         |                          |
                         |  Brand Protection System |
                         |                          |
(Clicks, Impressions) -> |  (Analysis & Filtering)  | -> [INVALID TRAFFIC] -> Blocked/Logged
                         |                          |
                         +--------------------------+
                                     |
                                     └─ [Feedback Loop to Update Rules]

🧠 Core Detection Logic

Example 1: IP Address Blacklisting

This logic checks the source IP address of a click against a known database of fraudulent or suspicious IPs. It is one of the most fundamental layers of traffic protection, often used to block traffic from data centers, VPNs, or proxies commonly used by bots.

FUNCTION check_ip(ip_address):
  IF ip_address IN known_fraudulent_ips_database:
    RETURN "BLOCK"
  ELSE:
    RETURN "ALLOW"

Example 2: Click Timestamp Analysis

This logic analyzes the time between clicks from the same user or IP to identify non-human patterns. A human user typically has a natural delay between actions, whereas a bot might generate clicks much faster than a person could, indicating automated fraud.

FUNCTION analyze_click_timing(user_id, click_timestamp):
  last_click_time = get_last_click_time(user_id)
  time_difference = click_timestamp - last_click_time
  
  IF time_difference < MINIMUM_CLICK_INTERVAL:
    RETURN "FLAG_AS_SUSPICIOUS"
  ELSE:
    RETURN "VALID"

Example 3: User-Agent Validation

This logic inspects the user-agent string sent by the browser or device. Many bots use outdated, generic, or known fraudulent user agents. This check helps filter out automated traffic that fails to impersonate a legitimate, modern web browser successfully.

FUNCTION validate_user_agent(user_agent_string):
  IF user_agent_string IS NULL or user_agent_string IN known_bot_user_agents:
    RETURN "BLOCK"
  IF contains_suspicious_keywords(user_agent_string, ["bot", "spider", "crawler"]):
    RETURN "FLAG_AS_SUSPICIOUS"
  ELSE:
    RETURN "VALID"

📈 Practical Use Cases for Businesses

Businesses use Brand Protection to safeguard their digital advertising investments and maintain data integrity. By filtering out fraudulent traffic, companies can achieve more accurate campaign metrics, improve their return on ad spend, and protect their brand's reputation from being associated with low-quality or harmful websites.

• Campaign Shielding – Protects active PPC and social media ad campaigns from budget depletion caused by automated bots and click farms, ensuring that ad spend reaches real potential customers.
• Data Integrity – Ensures that website analytics and campaign performance data are not skewed by fake traffic, leading to more accurate insights and better-informed marketing decisions.
• Reputation Management – Prevents ads from appearing on inappropriate or harmful websites, which could damage the brand's image and erode consumer trust.
• ROAS Optimization – Improves Return on Ad Spend (ROAS) by eliminating wasteful spending on fraudulent clicks and impressions, thereby increasing the efficiency of the advertising budget.

Example 1: Geolocation Filtering Rule

This logic is used to block traffic from geographic locations where the business does not operate or has identified high levels of fraudulent activity. It is a common practice for local or national businesses to avoid wasting their ad budget on international clicks.

FUNCTION check_geolocation(ip_address):
  user_country = get_country_from_ip(ip_address)
  
  IF user_country NOT IN allowed_countries_list:
    // Block traffic from outside the target market
    log_event("Blocked due to geo-restriction", ip_address, user_country)
    RETURN "BLOCK"
  ELSE:
    RETURN "ALLOW"

Example 2: Session Behavior Scoring

This logic analyzes a user's behavior within a session to determine if it is human-like. It scores factors such as mouse movement, scroll depth, and time on page. A session with no mouse movement and instant bounces is likely a bot and would receive a high fraud score.

FUNCTION score_session_behavior(session_data):
  fraud_score = 0
  
  IF session_data.mouse_events < 5:
    fraud_score += 30
  
  IF session_data.time_on_page < 2_seconds:
    fraud_score += 40
    
  IF session_data.scroll_depth_percent < 10:
    fraud_score += 20
    
  // A score above a certain threshold indicates fraud
  IF fraud_score > 50:
    RETURN "INVALID"
  ELSE:
    RETURN "VALID"

🐍 Python Code Examples

This Python code demonstrates a simple way to detect abnormally frequent clicks from a single IP address. It maintains a record of recent clicks and flags an IP if its click frequency exceeds a defined threshold, a common sign of bot activity.

from collections import deque
import time

CLICK_HISTORY = {}
TIME_WINDOW_SECONDS = 60
MAX_CLICKS_IN_WINDOW = 10

def is_click_fraud(ip_address):
    current_time = time.time()
    
    if ip_address not in CLICK_HISTORY:
        CLICK_HISTORY[ip_address] = deque()

    # Remove clicks older than the time window
    while (CLICK_HISTORY[ip_address] and 
           current_time - CLICK_HISTORY[ip_address] > TIME_WINDOW_SECONDS):
        CLICK_HISTORY[ip_address].popleft()
        
    # Add current click and check count
    CLICK_HISTORY[ip_address].append(current_time)
    
    if len(CLICK_HISTORY[ip_address]) > MAX_CLICKS_IN_WINDOW:
        print(f"Fraud detected from IP: {ip_address}")
        return True
        
    return False

# Simulate clicks
is_click_fraud("192.168.1.100") # Returns False
# ... rapid clicks from same IP ...
is_click_fraud("192.168.1.100") # Will eventually return True

This example provides a function to filter traffic based on suspicious user-agent strings. It checks if the user agent is on a blocklist or is missing entirely, which are common indicators of low-quality or automated traffic sources.

KNOWN_BOT_AGENTS = [
    "AhrefsBot",
    "SemrushBot",
    "MJ12bot",
    "DotBot"
]

def filter_by_user_agent(user_agent):
    if not user_agent:
        print("Blocking request with no user agent.")
        return False # Block
        
    for bot_agent in KNOWN_BOT_AGENTS:
        if bot_agent.lower() in user_agent.lower():
            print(f"Blocking known bot: {user_agent}")
            return False # Block
            
    return True # Allow

# Simulate checks
filter_by_user_agent("Mozilla/5.0 (Windows NT 10.0; Win64; x64)...") # True
filter_by_user_agent("Mozilla/5.0 (compatible; AhrefsBot/7.0; +http://ahrefs.com/robot/)") # False

Types of Brand Protection

• Proactive Filtering – This method involves setting up pre-bid rules and filters to prevent ads from being served to suspicious users or on inappropriate sites in the first place. It relies on blacklists, whitelists, and keyword blocking to preemptively avoid fraud and brand-safety issues.
• Reactive Analysis – This type involves analyzing traffic data after clicks or impressions have already occurred. It focuses on identifying patterns of fraud in campaign reports and using that data to request refunds from ad networks and update proactive filters for the future.
• AI and Machine Learning-Based Detection – This advanced approach uses algorithms to learn the patterns of normal user behavior and identify anomalies in real time. It is more adaptive than static rule-based systems and can detect new and evolving types of fraud that may otherwise go unnoticed.
• Content Verification – This type focuses specifically on brand safety by scanning the content of web pages where ads are placed. It ensures that a brand's ads do not appear next to content that is offensive, illegal, or otherwise contrary to the brand's values.
• Affiliate Fraud Protection – This is a specialized form of brand protection focused on monitoring the traffic sent by affiliate marketers. It detects policy violations such as trademark bidding or sending incentivized traffic, ensuring affiliates are promoting the brand in a compliant manner.

How Brand safety Works

Incoming Ad Request → +-------------------------+ → Ad Server
                     │   Brand Safety System   │
                     +-------------------------+
                          │           │
                          │           └─→ [Block]
                          ↓
  [Pre-Bid Analysis]───────+
      │      │      │
      │      │      └─ Content Analysis (Keywords, Topics)
      │      └─ Publisher Vetting (Blacklists/Whitelists)
      └─ Traffic Source Analysis (IP, User-Agent)

🧠 Core Detection Logic

Example 1: Content Category Filtering

This logic prevents ads from appearing on pages with undesirable content. By categorizing web pages based on topics like “Hate Speech” or “Adult Content,” advertisers can exclude entire segments of the web, reducing the risk of negative brand association and exposure to non-brand-safe environments.

FUNCTION checkContent(page_url, ad_campaign):
  // Get content categories for the URL
  content_categories = getCategories(page_url)

  // Get campaign's excluded categories
  excluded_categories = ad_campaign.exclusions

  // Check for overlap
  FOR category IN content_categories:
    IF category IN excluded_categories:
      RETURN "BLOCK_AD" // Unsafe placement

  RETURN "SERVE_AD" // Safe placement

Example 2: IP Blacklisting

This technique blocks traffic from known fraudulent sources. IP blacklists contain addresses of data centers, proxy servers, and known bot operators. By checking an incoming click’s IP against this list, the system can reject non-human or malicious traffic before it registers as a valid click, directly preventing click fraud.

FUNCTION isFraudulentIP(user_ip):
  // Load the blacklist of known fraudulent IPs
  ip_blacklist = loadBlacklist("fraud_ips.txt")

  IF user_ip IN ip_blacklist:
    RETURN TRUE // Fraudulent IP detected

  RETURN FALSE

Example 3: Session Click Velocity

This heuristic identifies non-human behavior by tracking the number of clicks from a single user session within a short time frame. A sudden, high frequency of clicks is a strong indicator of an automated script or bot. This rule helps mitigate automated click fraud that simple IP checks might miss.

FUNCTION checkClickVelocity(session_id, time_window_seconds):
  // Get all clicks for this session
  session_clicks = getClicks(session_id)

  // Filter clicks within the specified time window
  recent_clicks = filterByTime(session_clicks, time_window_seconds)

  // Define a threshold for suspicious frequency
  click_threshold = 5 

  IF count(recent_clicks) > click_threshold:
    RETURN "FLAG_AS_FRAUD"

  RETURN "VALID_TRAFFIC"

📈 Practical Use Cases for Businesses

• Campaign Shielding – Businesses use brand safety to automatically block their ads from appearing on websites, videos, or apps with harmful or inappropriate content, protecting their reputation and preventing wasted ad spend.
• Fraud Prevention – By filtering out non-human bot traffic and clicks from known fraudulent sources, companies ensure their advertising budget is spent on reaching real potential customers, not on fake engagements.
• Improved Analytics – Brand safety ensures that marketing data is clean and accurate. By removing fraudulent clicks and impressions, businesses can make better decisions based on genuine user engagement, leading to a higher return on ad spend.
• Supply Chain Transparency – Companies can use tools like ads.txt and sellers.json to verify that they are buying ad inventory from authorized sellers, reducing the risk of domain spoofing and ensuring ads appear on legitimate publisher sites.

Example 1: Geographic Fencing Rule

This logic prevents clicks from regions outside the campaign’s target area, a common tactic in click fraud where click farms are located in different countries. This ensures the ad budget is spent on the intended audience.

FUNCTION isValidGeo(user_ip, campaign_target_countries):
  user_country = getCountryFromIP(user_ip)

  IF user_country NOT IN campaign_target_countries:
    // Block click and log as geographic mismatch
    logEvent("GEO_MISMATCH_FRAUD", user_ip, user_country)
    RETURN FALSE

  RETURN TRUE

Example 2: Session Anomaly Scoring

This logic scores a user session based on multiple behavioral attributes. A session with no mouse movement, instant clicks, and a 100% bounce rate would receive a high fraud score and be blocked. This is effective against sophisticated bots that mimic some human behavior.

FUNCTION calculateSessionScore(session_data):
  score = 0
  
  IF session_data.has_no_mouse_events:
    score += 40
  
  IF session_data.time_on_page < 2: // Less than 2 seconds
    score += 30
    
  IF session_data.is_from_known_data_center:
    score += 30

  // If score is above a certain threshold, flag as fraud
  IF score > 75:
    RETURN "FRAUDULENT"
    
  RETURN "LEGITIMATE"

🐍 Python Code Examples

This Python function checks if a user agent string belongs to a known bot or a non-standard browser, which is a common sign of fraudulent traffic. Filtering based on user agents helps remove simple bots from campaign traffic.

def is_suspicious_user_agent(user_agent_string):
    """
    Checks if a user agent is on a blocklist of known bots or crawlers.
    """
    suspicious_agents = [
        "bot", "crawler", "spider", "headlesschrome"
    ]
    
    lower_ua = user_agent_string.lower()
    
    for agent in suspicious_agents:
        if agent in lower_ua:
            return True
            
    return False

# Example usage:
ua = "Mozilla/5.0 (compatible; Googlebot/2.1; +http://www.google.com/bot.html)"
print(f"Is suspicious: {is_suspicious_user_agent(ua)}")

This code analyzes click timestamps from the same IP address to detect abnormally high click frequencies. If an IP generates more clicks than a set threshold in a short period, it’s flagged as potential click fraud, a behavior typical of automated scripts.

from collections import defaultdict
import time

CLICK_LOGS = defaultdict(list)
TIME_WINDOW = 60  # seconds
CLICK_THRESHOLD = 10

def record_click(ip_address):
    """Records a click timestamp for a given IP."""
    current_time = time.time()
    CLICK_LOGS[ip_address].append(current_time)

def is_click_fraud(ip_address):
    """Checks if the IP has exceeded the click threshold in the time window."""
    current_time = time.time()
    
    # Filter out old timestamps
    recent_clicks = [t for t in CLICK_LOGS[ip_address] if current_time - t < TIME_WINDOW]
    CLICK_LOGS[ip_address] = recent_clicks
    
    if len(recent_clicks) > CLICK_THRESHOLD:
        return True
        
    return False

# Example usage:
record_click("192.168.1.100")
print(f"Is fraud: {is_click_fraud('192.168.1.100')}")

Types of Brand safety

• Content-Level Filtering – This type focuses on the context of a specific page or video. It uses keyword blocking and topic analysis to prevent ads from appearing next to content categories deemed unsafe, such as violence, hate speech, or fake news.
• Domain-Level Blocking – This method involves maintaining blacklists of entire websites or apps known to host inappropriate content or engage in fraudulent activities. It provides a broader but less granular layer of protection by blocking placements across an entire domain.
• Behavioral Anomaly Detection – This type analyzes user behavior patterns to identify non-human traffic. It flags suspicious activities like high click frequency, impossibly fast browsing, or traffic from known data centers, which are strong indicators of bot-driven click fraud.
• Pre-Bid Verification – This is a proactive approach where inventory is analyzed for brand safety risks *before* an ad bid is placed. It leverages third-party data to evaluate if a potential impression meets an advertiser’s safety and viewability standards, preventing bids on fraudulent or unsafe placements.
• AI and Machine Learning Analysis – This advanced type uses AI to understand content nuance, sentiment, and visual context beyond simple keywords. It can distinguish between a news report about a tragedy and content that promotes violence, offering more sophisticated and accurate protection.

How Broadcaster Video on Demand Works

  User Request         │        Ad Decision Engine         │       Content & Ad Delivery
┌──────────────────┐   │   ┌─────────────────────────────┐   │   ┌────────────────────────┐
│ User selects     ├───────►│ 1. Authenticate User        ├───────►│ Stream Content + Ad    │
│ content on       │   │   │    (Device ID, User Agent)  │   │   │ to authenticated user  │
│ BVOD platform    │   │   └─────────────────────────────┘   │   └────────────────────────┘
└──────────────────┘   │                  │                  │
                       │                  ▼                  │
                       │   ┌─────────────────────────────┐   │
                       │   │ 2. Analyze Request          ├─┐ │
                       │   │    (IP, Geo, Time)          │ │ │
                       │   └─────────────────────────────┘ │ │
                       │                  │                │ │
                       │                  ▼                │ │
                       │   ┌─────────────────────────────┐ │ │
                       │   │ 3. Check against Fraud Rules│ │ │
                       │   │    (e.g., blocklists,       │ │ │
                       │   │     frequency caps)         │ │ │
                       │   └─────────────────────────────┘ │ │
                       │                  │                │ │
                       │                  ▼                │ │
                       │   ┌─────────────────────────────┐ │ │
                       │   │ 4. Final Verdict:           ├─┘ │
                       │   │    Allow or Block           │   │
                       │   └─────────────────────────────┘   │

🧠 Core Detection Logic

Example 1: IP Blocklisting

This logic prevents traffic from known fraudulent sources. When a request comes in, its IP address is checked against a database of addresses associated with data centers, VPNs, or past fraudulent activity. This is a first line of defense in a traffic protection system.

function isFraudulent(request) {
  const ip = request.getIp();
  if (isKnownDataCenter(ip) || isBlacklisted(ip)) {
    return true; // Block request
  }
  return false;
}

Example 2: Session Heuristics

This logic analyzes user behavior within a single session to spot anomalies. It looks at the time between clicks, page interaction, and navigation flow. Unusually fast clicks or a lack of typical user engagement can indicate a bot. This fits within the behavioral analysis layer of traffic protection.

function analyzeSession(session) {
  const clickTimes = session.getClickTimestamps();
  if (clickTimes.length > 1) {
    const timeDiff = clickTimes - clickTimes;
    if (timeDiff < 200) { // Less than 200ms
      return "suspicious";
    }
  }
  return "legitimate";
}

Example 3: Geo Mismatch Detection

This logic compares the geographical location of the IP address with other user data, such as their stated region or timezone settings. A significant mismatch can suggest the use of a proxy or a compromised device. This is often used to enforce content licensing and detect sophisticated fraud.

function checkGeoMismatch(request) {
  const ipGeo = getGeoFromIp(request.getIp());
  const userProfileGeo = request.getUserProfile().getCountry();
  if (ipGeo !== userProfileGeo) {
    logSuspiciousActivity("Geo Mismatch", request);
    return true;
  }
  return false;
}

📈 Practical Use Cases for Businesses

Businesses use Broadcaster Video on Demand to ensure their advertising budget is spent on real, engaged viewers. It provides a brand-safe environment with professionally produced content, which enhances campaign effectiveness. By leveraging the detailed viewership data from BVOD platforms, companies can refine their targeting and improve their return on ad spend, knowing that their ads are being seen by genuine customers in a trusted setting.

• Campaign Shielding – Protects ad campaigns from invalid traffic and bots by running them in a closed, monitored environment, maximizing budget efficiency.
• Clean Analytics – Ensures marketing analytics are based on real human interactions, leading to more accurate insights and better strategic decisions.
• Improved ROI – Increases return on investment by placing ads in premium, brand-safe content where viewers are more engaged and receptive to advertising.
• Audience Verification – Guarantees that ads are served to the intended demographic by using the broadcasters' first-party data for precise audience targeting.

Example 1: Geofencing Rule

function applyGeofencing(user) {
  const allowedCountries = ["US", "CA", "GB"];
  const userCountry = getCountryFromIP(user.ip_address);

  if (!allowedCountries.includes(userCountry)) {
    blockAdRequest(user.id);
    logEvent("Blocked", "Geo-fence", user.ip_address);
  } else {
    serveAd(user.id);
  }
}

Example 2: Session Scoring Logic

function scoreSession(session) {
  let score = 0;
  // High engagement (e.g., video completion) is a good sign
  if (session.videoCompletion > 0.9) {
    score += 10;
  }
  // Multiple rapid-fire ad clicks are a bad sign
  if (session.adClicks > 3 && session.duration < 60) {
    score -= 20;
  }
  return score;
}

🐍 Python Code Examples

This Python code filters incoming web traffic by checking if the IP address is in a known blocklist of fraudulent actors. This is a common first step in any ad fraud detection system to weed out obviously bad traffic before it consumes resources.

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

# Example Usage
fraudulent_ips = {"1.2.3.4", "5.6.7.8"}
incoming_ip = "1.2.3.4"
filter_blocked_ips(incoming_ip, fraudulent_ips)

The following code analyzes the frequency of clicks from a single user to identify behavior that is too fast to be human. This helps in detecting automated bots that are programmed to click on ads at an inhuman rate.

import time

def detect_abnormal_click_frequency(user_session):
    """
    Detects if clicks are happening too quickly.
    """
    click_timestamps = user_session.get("clicks", [])
    if len(click_timestamps) < 2:
        return False

    time_diff = click_timestamps[-1] - click_timestamps[-2]
    if time_diff < 0.5:  # Less than 500 milliseconds
        print("Abnormal click frequency detected!")
        return True
    return False

# Example Usage
session = {"user_id": "user-123", "clicks": [time.time()]}
time.sleep(0.2)
session["clicks"].append(time.time())
detect_abnormal_click_frequency(session)

This example scores traffic based on the user agent string provided by the browser. Suspicious user agents, such as those that are outdated or known to be used by bots, receive a lower score, helping to filter out non-human traffic.

def score_traffic_by_user_agent(user_agent):
    """
    Scores traffic based on the user agent string.
    """
    score = 100
    if not user_agent or "bot" in user_agent.lower():
        score = 0
    elif "headless" in user_agent.lower():
        score = 10
    
    print(f"User agent '{user_agent}' scored: {score}")
    return score

# Example Usage
suspicious_ua = "Mozilla/5.0 (compatible; Googlebot/2.1; +http://www.google.com/bot.html)"
score_traffic_by_user_agent(suspicious_ua)

Types of Broadcaster Video on Demand

• Server-Side Ad Insertion (SSAI) – This type stitches ads directly into the video stream on the server side before it reaches the user's device. This makes the ads difficult for ad-blockers to detect and remove, ensuring a seamless viewing experience and protecting ad revenue from being lost.
• Client-Side Ad Insertion (CSAI) – In this method, the video player on the user's device requests ads from an ad server separately from the content. While more susceptible to ad blockers, it allows for more personalized and targeted advertising based on data available on the client side.
• Hybrid Models – This approach combines elements of both SSAI and CSAI. For instance, some ads might be stitched into the stream server-side for all viewers, while other ad slots are filled client-side to allow for dynamic, targeted advertising. This provides a balance between robust ad delivery and personalization.
• Authenticated Viewing – This type requires users to log in before accessing content. This provides valuable first-party data, allowing for highly targeted advertising and a much lower risk of fraud, as user accounts can be monitored for suspicious activity over time.

How Budget Allocation Works

Incoming Ad Traffic
        │
        ▼
+---------------------+
│   Pre-Filtering     │
│  (IP/UA Checks)     │
+---------------------+
        │
        ▼
+-------------------------+
│ Budget Pacing Analysis  │
│(Spend Velocity vs. Time)│
+-------------------------+
        │
        ▼
+---------------------+
│  Risk Scoring Engine  │
+---------------------+
        │
        ├─ Legitimate Traffic → +--------------+
        │                      │ Allow & Log  │
        │                      +--------------+
        │
        └─ Suspicious Traffic → +--------------+
                               │ Block & Flag │
                               +--------------+

🧠 Core Detection Logic

Example 1: Budget Velocity Threshold

This logic prevents runaway budget depletion from automated attacks. It works by setting a maximum spend rate (e.g., dollars per minute) for a campaign. If incoming clicks cause the spend rate to exceed this threshold, the system temporarily throttles or blocks traffic from the sources contributing most to the spike.

FUNCTION check_budget_velocity(campaign_id, current_spend_rate):
  MAX_SPEND_PER_MINUTE = get_campaign_threshold(campaign_id)

  IF current_spend_rate > MAX_SPEND_PER_MINUTE THEN
    // Identify top contributing IPs or sources in the last minute
    offending_sources = get_top_sources_by_spend(campaign_id, 1_minute_window)

    // Temporarily block the most aggressive sources
    FOR source IN offending_sources:
      add_to_blocklist(source, duration=60_minutes)
    
    RETURN "BLOCK"
  ELSE
    RETURN "ALLOW"
  END IF
END FUNCTION

Example 2: Geographic Budget Allocation Anomaly

This logic detects fraud by matching click locations to budget allocation. If a campaign’s budget is allocated primarily to one country, but a high volume of clicks arrives from another, it signals a likely attempt at fraud or a misconfiguration. The system flags or blocks traffic from non-budgeted regions.

FUNCTION check_geo_budget_match(click_geo, campaign_id):
  // Get budget allocation rules for the campaign
  budgeted_regions = get_budgeted_geos(campaign_id) // e.g., {"US": 80%, "CA": 20%}

  // Check if the click's geography has an allocated budget
  IF click_geo NOT IN budgeted_regions.keys() THEN
    log_event("Fraudulent click from non-budgeted geo", click_geo)
    RETURN "BLOCK"
  ELSE
    RETURN "ALLOW"
  END IF
END FUNCTION

Example 3: Time-of-Day Pacing Anomaly

This logic identifies fraud by comparing spending patterns to expected user activity times. If a campaign historically sees most of its conversions during business hours, but the budget begins draining rapidly at 3 AM, the system flags this as a temporal anomaly indicative of bot traffic.

FUNCTION check_time_pacing(campaign_id, current_time):
  // Get the campaign's expected active hours (e.g., 9:00 - 17:00)
  active_hours = get_campaign_active_window(campaign_id)
  
  // Get the current spend rate
  spend_rate = get_current_spend_rate(campaign_id)
  
  // Check for high spend outside of normal hours
  IF current_time NOT IN active_hours AND spend_rate > LOW_THRESHOLD THEN
    log_event("High spend detected outside of active hours", campaign_id)
    // Reduce bidding or trigger manual review
    set_bidding_multiplier(campaign_id, 0.1)
    RETURN "FLAG_FOR_REVIEW"
  ELSE
    RETURN "ALLOW"
  END IF
END FUNCTION

📈 Practical Use Cases for Businesses

• Campaign Shielding – Protects ad budgets by automatically blocking traffic sources that cause sudden, unnatural spikes in spending, preserving funds for legitimate users.
• ROAS Optimization – Improves Return on Ad Spend by ensuring that budget is allocated to channels and times that historically yield high-quality, converting traffic, not wasted on fraudulent clicks.
• Analytics Integrity – Ensures marketing data is clean and reliable by filtering out the noise from bots and fake users. This leads to more accurate campaign metrics and better strategic decisions.
• Competitive Protection – Prevents malicious competitors from intentionally clicking on ads to deplete budgets and knock campaigns offline for the day.

Example 1: Geofencing Rule

A business targeting customers only in the UK can use a geofencing rule tied to its budget. Any clicks originating from outside the UK are automatically blocked, as no budget is allocated to those regions, preventing international click farms from wasting ad spend.

// Rule: Block traffic if its geo-location has no budget allocation.
RULE "GEO_BUDGET_GUARD"
WHEN
  click.country_code NOT IN campaign.budget.allocated_countries
THEN
  ACTION: BLOCK
  REASON: "No budget allocated for this region."
END RULE

Example 2: Session Spend Velocity Cap

To stop a single bot from depleting a budget, a rule can cap the number of paid clicks allowed from a single user session within a short time frame. If a session exceeds 3 clicks in 5 minutes, subsequent clicks from that session are invalidated.

// Rule: Limit click frequency from a single session.
RULE "SESSION_VELOCITY_CAP"
WHEN
  session.click_count > 3 AND session.duration < 300_seconds
THEN
  ACTION: BLOCK
  REASON: "Exceeded click velocity for a single session."
END RULE

🐍 Python Code Examples

This Python function simulates checking if a specific IP address is clicking too frequently, a common sign of bot activity that rapidly depletes ad budgets. It tracks click timestamps to determine if the rate exceeds a safe limit.

from collections import defaultdict
import time

CLICK_HISTORY = defaultdict(list)
FREQUENCY_LIMIT = 5  # Max clicks
TIME_WINDOW = 60  # In seconds

def is_click_frequency_abnormal(ip_address):
    """Checks if an IP's click frequency is too high."""
    current_time = time.time()
    
    # Filter out old clicks from this IP's history
    CLICK_HISTORY[ip_address] = [t for t in CLICK_HISTORY[ip_address] if current_time - t < TIME_WINDOW]
    
    # Add the current click
    CLICK_HISTORY[ip_address].append(current_time)
    
    # Check if the number of recent clicks exceeds the limit
    if len(CLICK_HISTORY[ip_address]) > FREQUENCY_LIMIT:
        print(f"Blocking IP {ip_address} for abnormal click frequency.")
        return True
        
    print(f"IP {ip_address} is within normal frequency limits.")
    return False

# Example usage:
is_click_frequency_abnormal("192.168.1.101")
is_click_frequency_abnormal("192.168.1.101")

This code demonstrates a simple campaign budget monitor. It simulates spending and blocks traffic once the budget is exhausted, a fundamental feature for preventing overspending due to fraudulent or unexpected traffic surges.

class CampaignBudget:
    def __init__(self, campaign_id, total_budget):
        self.campaign_id = campaign_id
        self.total_budget = total_budget
        self.spent_budget = 0.0

    def record_click(self, cost_per_click):
        """Records a click and checks if the budget is depleted."""
        if self.spent_budget + cost_per_click > self.total_budget:
            print(f"Campaign {self.campaign_id} budget depleted. Blocking further traffic.")
            return False
        
        self.spent_budget += cost_per_click
        print(f"Click recorded for {self.campaign_id}. Total spent: ${self.spent_budget:.2f}")
        return True

# Example usage:
campaign_a = CampaignBudget("summer_sale", 100.0)
for _ in range(55): # Simulate 55 clicks at $2 each
    if not campaign_a.record_click(2.0):
        break

Types of Budget Allocation

• Static Allocation – A fixed budget is assigned to campaigns, channels, or time periods. Fraud detection is based on rigid thresholds, such as blocking all traffic after a daily budget is spent, which is simple but inflexible against dynamic threats.
• Dynamic Pacing – Budgets are allocated algorithmically throughout the day or campaign duration based on real-time performance and traffic quality. The system slows down or accelerates spending based on conversion data and fraud signals, making it more resilient to attacks.
• Rule-Based Allocation – Budgets are governed by a set of "if-then" rules. For example, if traffic from a certain publisher shows a high bounce rate and low conversion, the system automatically reduces or cuts off the budget allocated to that source.
• Predictive Allocation – Utilizes machine learning to forecast traffic quality and potential fraud. It allocates budget towards segments predicted to have high engagement and away from those with characteristics matching known fraudulent patterns, preventing waste before it happens.

How Campaign Audit Works

Incoming Traffic (Clicks/Impressions)
           │
           ▼
+---------------------+
│   Data Collection   │
│ (IP, UA, Timestamp) │
+---------------------+
           │
           ▼
+---------------------+
│   Real-Time Analysis│
│ (Rules & Heuristics)│
+---------------------+
           │
           ▼
+---------------------+
│  Scoring & Flagging │
│ (Valid vs. Invalid) │
+---------------------+
           │
           ├─→ [ Valid Traffic ] ───> To Your Website
           │
           └─→ [ Invalid Traffic ] ──> Blocked / Reported

🧠 Core Detection Logic

Example 1: Repetitive Click Filtering

This logic prevents a single user (or bot) from clicking the same ad multiple times to deplete a campaign’s budget. It works by tracking the frequency of clicks from individual IP addresses or device IDs over a short time frame and blocking them after a reasonable threshold is exceeded.

FUNCTION repetitive_click_filter(click_event):
  IP_address = click_event.ip
  timestamp = click_event.time
  
  // Define time window (e.g., 5 minutes) and click limit (e.g., 3 clicks)
  time_window = 300 // seconds
  click_limit = 3
  
  // Get recent clicks from this IP
  recent_clicks = get_clicks_from_ip(IP_address, within_last=time_window)
  
  IF count(recent_clicks) >= click_limit:
    FLAG as "FRAUDULENT_REPETITIVE_CLICK"
    BLOCK click_event
  ELSE:
    ALLOW click_event
  END IF
END FUNCTION

Example 2: Data Center & Proxy Detection

This logic blocks traffic originating from data centers, servers, or public proxies, which are commonly used by bots to hide their true origin. It works by checking the click’s IP address against known lists of non-residential IP ranges.

FUNCTION datacenter_ip_check(click_event):
  IP_address = click_event.ip
  
  // Load list of known data center IP ranges
  datacenter_ips = load_datacenter_ip_list()
  
  IF IP_address is in datacenter_ips:
    FLAG as "FRAUDULENT_DATACENTER_IP"
    BLOCK click_event
  ELSE:
    ALLOW click_event
  END IF
END FUNCTION

Example 3: Behavioral Anomaly Detection

This logic analyzes user behavior post-click to identify non-human patterns. Clicks that result in an immediate bounce (less than 1-second session duration) or show no mouse movement are flagged as suspicious, as this behavior is typical of automated bots, not genuine users.

FUNCTION behavioral_anomaly_check(session_data):
  session_duration = session_data.duration // in seconds
  mouse_movement_events = session_data.mouse_events
  
  // Define behavioral thresholds
  min_duration = 1 // second
  min_mouse_events = 1
  
  IF session_duration < min_duration AND count(mouse_movement_events) < min_mouse_events:
    FLAG as "FRAUDULENT_BEHAVIOR_BOT"
    RECORD as invalid_session
  ELSE:
    RECORD as valid_session
  END IF
END FUNCTION

📈 Practical Use Cases for Businesses

• Budget Protection – Automatically blocks fake clicks from bots and competitors, preventing wasted ad spend and ensuring marketing funds are spent on reaching real potential customers.
• Data Integrity – Filters out invalid traffic from analytics reports. This provides a clear and accurate view of campaign performance, conversion rates, and true user engagement.
• Improved ROAS – By eliminating fraudulent traffic that never converts, Campaign Audit helps improve the return on ad spend (ROAS) by focusing the budget on genuine, high-intent audiences.
• Lead Generation Shielding – Prevents bots from submitting fake forms, ensuring that sales and marketing teams receive genuine leads and aren't wasting time on fraudulent submissions.

Example 1: Geolocation Mismatch Rule

This logic is used to block clicks that appear to originate from outside a campaign's targeted geographic area, a common tactic used by click farms employing proxies.

// Use Case: A campaign targets users only in California, USA.
FUNCTION check_geo_mismatch(click_data, campaign_rules):
  user_ip = click_data.ip_address
  user_location = get_location_from_ip(user_ip) // e.g., "India"
  
  target_location = campaign_rules.geo_target // e.g., "California, USA"
  
  IF user_location is NOT IN target_location:
    // Block click and flag for review
    BLOCK click_data
    LOG "Geo Mismatch Fraud: Click from " + user_location
  ELSE:
    // Allow click
    PROCEED click_data
  END IF
END FUNCTION

Example 2: Session Scoring Logic

This logic assigns a score to each user session based on multiple interactions. A low score indicates bot-like behavior, while a high score suggests a legitimate user. This is useful for identifying sophisticated bots that mimic some human actions.

// Use Case: Distinguish between low-quality traffic and engaged users.
FUNCTION score_user_session(session_events):
  score = 0
  
  // Score based on time on page
  IF session_events.time_on_page > 30 seconds:
    score += 10
  ELSE IF session_events.time_on_page < 2 seconds:
    score -= 20
  
  // Score based on interaction
  IF session_events.scrolled_page_percentage > 50:
    score += 15
  
  IF session_events.clicked_internal_link > 0:
    score += 20
  
  // Evaluate final score
  IF score < 10:
    FLAG session as "LOW_QUALITY"
  ELSE:
    FLAG session as "HIGH_QUALITY"
  END IF
  
  RETURN score
END FUNCTION

🐍 Python Code Examples

This code demonstrates how to filter out clicks that come from a predefined list of suspicious IP addresses, often associated with bots or data centers.

# List of known fraudulent IP addresses
BLACKLISTED_IPS = {"198.51.100.1", "203.0.113.10", "192.0.2.55"}

def filter_by_ip_blacklist(click_ip):
    """Checks if a click's IP is in the blacklist."""
    if click_ip in BLACKLISTED_IPS:
        print(f"Blocking fraudulent click from IP: {click_ip}")
        return False
    else:
        print(f"Allowing legitimate click from IP: {click_ip}")
        return True

# Simulate incoming clicks
clicks = [{"ip": "98.12.56.2"}, {"ip": "203.0.113.10"}]
for click in clicks:
    filter_by_ip_blacklist(click["ip"])

This example shows a simple way to detect a common form of bot activity: an abnormally high frequency of clicks from a single source in a short amount of time.

from collections import defaultdict
import time

CLICK_LOG = defaultdict(list)
TIME_WINDOW = 60  # seconds
CLICK_THRESHOLD = 5

def is_click_frequency_abnormal(ip_address):
    """Detects if click frequency from an IP exceeds a threshold."""
    current_time = time.time()
    
    # Remove old timestamps
    CLICK_LOG[ip_address] = [t for t in CLICK_LOG[ip_address] if current_time - t < TIME_WINDOW]
    
    # Add current click timestamp
    CLICK_LOG[ip_address].append(current_time)
    
    # Check if threshold is exceeded
    if len(CLICK_LOG[ip_address]) > CLICK_THRESHOLD:
        print(f"Abnormal click frequency detected from {ip_address}.")
        return True
    return False

# Simulate rapid clicks from one IP
for _ in range(6):
    is_click_frequency_abnormal("198.18.0.1")

Types of Campaign Audit

• Pre-Bid Audit – This audit happens automatically before an ad is even purchased. The system analyzes the ad placement opportunity (like the website and user data) and decides whether to bid on it, filtering out low-quality or fraudulent inventory from the start.
• Real-Time Click Analysis – This type of audit analyzes clicks as they happen. It uses fast-acting rules to check for red flags like known fraudulent IPs, suspicious user agents, or data center origins, blocking invalid traffic before it reaches the advertiser's site.
• Post-Click Behavioral Audit – This audit examines user behavior immediately after a click. It analyzes metrics like bounce rate, session duration, and on-page interactions (or lack thereof) to identify non-human patterns that real-time analysis might miss.
• Historical Pattern Analysis – This involves analyzing aggregated campaign data over time to find recurring patterns of fraud. By identifying trends, such as certain publishers or times of day consistently delivering invalid traffic, advertisers can make strategic adjustments and refine blocking rules.

How Campaign Optimization Works

Incoming Ad Traffic (Click/Impression)
           │
           ▼
+----------------------+
│ 1. Data Collection   │
│ (IP, UA, Behavior)   │
+----------------------+
           │
           ▼
+----------------------+
│ 2. Real-Time Analysis│
│ (Rule & Heuristic    │
│  Matching)           │
+----------------------+
           │
     ┌─────┴──────┐
     │            │
     ▼            ▼
+----------+  +-----------+
│ Invalid  │  │ Valid     │
│ Traffic  │  │ Traffic   │
+----------+  +-----------+
     │            │
     └─┐          │
       │          │
       ▼          ▼
+----------------------+
│ 3. Action & Feedback │
│ (Block / Allow)      │
+----------------------+
           │
           ▼
+----------------------+
│ 4. Reporting         │
│ (Analytics & Logs)   │
+----------------------+

🧠 Core Detection Logic

Example 1: IP Address Filtering

This logic checks the source IP address of a click against known blocklists, such as those containing data center or proxy server IPs, which are commonly used for bot traffic. It’s a foundational layer of defense that filters out obviously non-human traffic sources before they can interact with an ad.

FUNCTION checkIP(ip_address):
  IF ip_address IN known_datacenter_ips:
    RETURN "BLOCK"
  IF ip_address IN known_proxy_ips:
    RETURN "BLOCK"
  IF ip_address IN user_defined_blocklist:
    RETURN "BLOCK"
  
  RETURN "ALLOW"

Example 2: Session Heuristics

This logic analyzes the behavior of a user within a single session to identify patterns inconsistent with genuine human interest. For instance, an impossibly high number of clicks in a short period from the same user suggests automated activity. This helps catch bots that may have bypassed basic IP filters.

FUNCTION checkSession(session_data):
  click_count = session_data.getClickCount()
  time_since_first_click = session_data.getTimeElapsed()

  // More than 5 clicks in under 10 seconds is suspicious
  IF click_count > 5 AND time_since_first_click < 10:
    RETURN "BLOCK"

  // Immediate bounce (less than 1 second on page) is a red flag
  IF session_data.getTimeOnPage() < 1:
    RETURN "FLAG_AS_SUSPICIOUS"

  RETURN "ALLOW"

Example 3: Behavioral Rule Matching

This logic looks for non-human behavioral patterns, such as a complete lack of mouse movement before a click or clicks that always land on the exact same pixel coordinates. Real users exhibit slight variations in their interactions, while bots are often programmed with rigid, repetitive actions.

FUNCTION checkBehavior(behavior_data):
  mouse_moved = behavior_data.hasMouseMovement()
  click_coordinates = behavior_data.getClickXY()

  IF NOT mouse_moved AND click_coordinates == (100, 250):
    // Clicks with no mouse movement at a fixed coordinate are likely bots
    RETURN "BLOCK"
  
  IF behavior_data.isScrollingTooFast():
    RETURN "BLOCK"
    
  RETURN "ALLOW"

📈 Practical Use Cases for Businesses

• Campaign Shielding: Automatically blocks clicks from known malicious sources, data centers, and proxy servers, preserving the ad budget for real potential customers and preventing financial losses from fraud.
• Analytics Purification: Filters out bot and other invalid traffic from campaign reports. This ensures that performance metrics like Click-Through Rate (CTR) and conversion rates are accurate, enabling better strategic decisions.
• ROAS Improvement: By ensuring ads are shown primarily to genuine users, optimization increases the likelihood of conversions. This directly improves the Return on Ad Spend (ROAS) by reducing wasted expenditure on clicks that never had conversion potential.
• Lead Quality Enhancement: Prevents fake sign-ups and form submissions by blocking bots at the top of the funnel. This provides sales teams with higher-quality leads and prevents pollution of the marketing database.

Example 1: Geofencing Rule

This pseudocode demonstrates a common use case where a business wants to ensure its ads are only engaged by users within its target countries. Clicks from outside the defined regions are automatically blocked.

FUNCTION checkGeo(user_ip, allowed_countries):
  user_country = getCountryFromIP(user_ip)

  IF user_country NOT IN allowed_countries:
    log("Blocked click from non-target country: " + user_country)
    RETURN "BLOCK"
  
  RETURN "ALLOW"

// --- Implementation ---
allowed_countries = ["USA", "CAN", "GBR"]
user_ip = "198.51.100.24" // Example IP from an untargeted region
checkGeo(user_ip, allowed_countries)

Example 2: Session Score Rule

This logic scores a user's session based on multiple risk factors. A session accumulates points for suspicious activities, and if the total score exceeds a certain threshold, the user is blocked. This provides a more nuanced approach than a single rule.

FUNCTION calculateSessionScore(session_data):
  score = 0
  
  IF session_data.uses_vpn:
    score += 30
    
  IF session_data.is_headless_browser:
    score += 50
    
  IF session_data.click_frequency > 10 per minute:
    score += 20
  
  RETURN score

// --- Implementation ---
session_score = calculateSessionScore(current_user_session)
fraud_threshold = 60

IF session_score >= fraud_threshold:
  RETURN "BLOCK"
ELSE:
  RETURN "ALLOW"

🐍 Python Code Examples

This Python function simulates the detection of abnormally high click frequencies from a single IP address. It tracks click timestamps and flags an IP if it exceeds a defined rate limit, a common sign of bot activity.

import time

click_logs = {}
# { "ip_address": [timestamp1, timestamp2, ...], ... }

def is_click_fraud(ip_address, time_window=60, max_clicks=10):
    """Checks if an IP exceeds the click frequency threshold."""
    current_time = time.time()
    
    # Remove old timestamps
    if ip_address in click_logs:
        click_logs[ip_address] = [t for t in click_logs[ip_address] if current_time - t < time_window]
    
    # Add current click
    click_logs.setdefault(ip_address, []).append(current_time)
    
    # Check for fraud
    if len(click_logs[ip_address]) > max_clicks:
        print(f"Fraud detected for IP: {ip_address}")
        return True
        
    return False

# --- Simulation ---
for _ in range(15):
    is_click_fraud("192.168.1.10")

This example demonstrates filtering traffic based on suspicious user-agent strings. Bots often use generic, outdated, or known non-standard user agents, which can be identified and blocked to prevent automated traffic from interacting with ads.

def is_suspicious_user_agent(user_agent):
    """Identifies user agents known to be associated with bots."""
    suspicious_signatures = ["bot", "spider", "headless", "phantomjs"]
    
    ua_lower = user_agent.lower()
    
    for signature in suspicious_signatures:
        if signature in ua_lower:
            print(f"Suspicious UA detected: {user_agent}")
            return True
            
    # Block empty or very short user agents
    if len(ua_lower) < 20:
        print(f"Short/empty UA detected: {user_agent}")
        return True
        
    return False

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

is_suspicious_user_agent(ua_bot)
is_suspicious_user_agent(ua_human)

Types of Campaign Optimization

• Rule-Based Optimization: This type uses a predefined set of static rules to filter traffic. For example, it might block all clicks from a specific country or IP range, or any user with a known bot-like user agent string. It is effective against simple, known threats.
• Heuristic and Behavioral Optimization: This method analyzes user behavior patterns rather than just static data points. It looks at metrics like mouse movements, scroll speed, and time between clicks to determine if the interaction is human-like or automated. It is better at catching sophisticated bots that mimic human behavior.
• Machine Learning (ML) Optimization: This advanced type uses algorithms to analyze vast datasets and identify new or evolving fraud patterns automatically. It adapts over time, learning from new threats to predict and block fraudulent activity before it becomes widespread, offering the most proactive protection.
• Threshold-Based Optimization: This approach sets limits on certain metrics and blocks users who exceed them. For example, it might cap the number of clicks allowed from a single IP address in one day. It is useful for mitigating brute-force click attacks and low-level abuse.

How Campaign Tracking Works

  User Click on Ad      Tracking Link         Data Collection &       Fraud Detection          Action
+-------------------+   +----------------+    +-------------------+   +--------------------+   +------------+
|                   |   |                |    |                   |   |                    |   |            |
|  Campaign Ad      ├─→ │  Redirects with  ├─→  │   Aggregates      ├─→ │  Analyzes Data for ├─→ │  Allow or  │
| (e.g., Google Ad) │   │  Parameters    │    │   Click Data      │   │  Anomalies         │   │  Block Click │
|                   │   |  (UTMs, etc.)  │    │ (IP, UA, Timestamp) │   │  (Bots, Patterns)  │   |            |
+-------------------+   +----------------+    +-------------------+   +--------------------+   +------------+
                                                    │
                                                    └──────────────────→ Log for Reporting

🧠 Core Detection Logic

Example 1: Click Velocity Analysis

This logic detects rapid-fire clicks from a single source targeting the same campaign. It’s a frontline defense against basic bots and click farms that aim to deplete ad budgets quickly. By setting a threshold for the number of clicks allowed from one IP address within a specific timeframe, it identifies and blocks this automated, non-human behavior.

FUNCTION check_click_velocity(click_event):
  ip_address = click_event.ip
  campaign_id = click_event.campaign_id
  timestamp = click_event.timestamp

  // Get recent clicks for this IP and Campaign
  recent_clicks = get_clicks_from_db(ip_address, campaign_id, last_60_seconds)

  // Define the threshold
  IF count(recent_clicks) > 5:
    // Flag as fraudulent
    block_request(ip_address)
    log_event("Fraud Detected: High Click Velocity", click_event)
    RETURN FRAUDULENT
  ELSE:
    // Store this click and allow
    store_click_event(click_event)
    RETURN LEGITIMATE
  END IF

Example 2: Geographic Mismatch Detection

This rule verifies that a click’s origin matches the geographic targeting of an ad campaign. For instance, if a campaign is targeted exclusively to users in Germany, a click originating from an IP address in Vietnam would be flagged as suspicious. This helps prevent fraud from offshore click farms and bots operating outside the intended market.

FUNCTION check_geo_mismatch(click_event, campaign_rules):
  ip_address = click_event.ip
  campaign_target_geo = campaign_rules.target_countries

  // Get the location of the IP address
  click_geo = geo_lookup(ip_address).country

  // Check if the click's country is in the allowed list
  IF click_geo NOT IN campaign_target_geo:
    // Flag as fraudulent
    block_request(ip_address)
    log_event("Fraud Detected: Geographic Mismatch", click_event)
    RETURN FRAUDULENT
  ELSE:
    RETURN LEGITIMATE
  END IF

Example 3: Parameter Tampering Validation

This logic ensures that the tracking parameters (e.g., UTM codes) in the URL have not been altered or removed. Bots sometimes attempt to bypass tracking by manipulating these parameters. This check validates the integrity of the tracking URL, ensuring that all required campaign data is present and correctly formatted, which is often a sign of a legitimate, unaltered click.

FUNCTION validate_campaign_parameters(request_url):
  required_params = ["utm_source", "utm_campaign"]
  
  // Extract parameters from the URL
  url_params = get_query_parameters(request_url)

  // Check if all required parameters are present
  FOR param IN required_params:
    IF param NOT IN url_params OR url_params[param] IS EMPTY:
      // A required parameter is missing or empty
      log_event("Fraud Warning: Parameter Tampering", request_url)
      RETURN SUSPICIOUS
    END IF
  END FOR

  RETURN LEGITIMATE

📈 Practical Use Cases for Businesses

• Campaign Shielding – Prevents bots and competitors from clicking on ads, directly protecting Pay-Per-Click (PPC) budgets from being wasted on fraudulent traffic and ensuring that spending is allocated toward reaching genuine potential customers.
• Data Integrity for Analytics – By filtering out invalid clicks before they are recorded, campaign tracking ensures that marketing analytics platforms (like Google Analytics) reflect real user engagement. This leads to more accurate performance metrics and better-informed strategic decisions.
• Lead Quality Improvement – Blocks traffic from sources known for generating fake leads or low-quality form submissions. This helps sales teams focus on genuinely interested prospects, improving conversion rates and overall return on ad spend (ROAS).
• Competitor Click-Fraud Mitigation – Identifies and blocks patterns of behavior consistent with competitors attempting to exhaust a company’s advertising budget. This helps maintain a level playing field and ensures ad visibility for real customers.

Example 1: Data Center Traffic Blocking

This pseudocode rule automatically blocks traffic originating from known data centers, which are a common source of non-human bot traffic and proxy servers used to mask fraudulent activity.

FUNCTION check_data_center_traffic(click_event):
  ip_address = click_event.ip
  
  // Check IP against a known list of data center IP ranges
  IS_DATA_CENTER_IP = is_in_datacenter_database(ip_address)

  IF IS_DATA_CENTER_IP:
    block_request(ip_address)
    log_event("Blocked: Data Center IP", ip_address)
    RETURN BLOCKED
  ELSE:
    RETURN ALLOWED
  END IF

Example 2: Session Engagement Scoring

This pseudocode demonstrates a more advanced use case where a user’s session is scored based on behavior. A click from a specific campaign might be initially allowed, but if the user shows no meaningful engagement (like scrolling or mouse movement) within a few seconds, the source is flagged for future blocking.

FUNCTION score_session_engagement(session_data, campaign_id):
  // Collect engagement metrics after a short delay
  time_on_page = session_data.time_on_page_seconds
  scrolled_pixels = session_data.pixels_scrolled
  mouse_movements = session_data.mouse_events_count
  
  // Define minimum engagement thresholds
  MIN_TIME = 5 
  MIN_SCROLL = 100

  // Score the session
  IF time_on_page < MIN_TIME AND scrolled_pixels < MIN_SCROLL AND mouse_movements < 2:
    source_ip = session_data.ip
    // Add IP to a low-quality traffic watchlist for the campaign
    add_to_watchlist(source_ip, campaign_id)
    log_event("Low Engagement Score", source_ip, campaign_id)
    RETURN LOW_QUALITY
  ELSE:
    RETURN HIGH_QUALITY
  END IF

🐍 Python Code Examples

This code simulates detecting abnormal click frequency from a single IP address on a specific campaign. It helps block basic automated bots by tracking click timestamps and flagging sources that exceed a defined rate limit.

# Dictionary to store click timestamps for each IP/campaign pair
click_logs = {}
from collections import deque
import time

# Rate limiting settings: 5 clicks within 10 seconds
MAX_CLICKS = 5
TIME_WINDOW_SECONDS = 10

def is_click_fraudulent(ip_address, campaign_id):
    """Checks if a click from an IP for a campaign is fraudulent based on frequency."""
    current_time = time.time()
    key = (ip_address, campaign_id)

    if key not in click_logs:
        click_logs[key] = deque()

    # Remove timestamps outside the time window
    while click_logs[key] and click_logs[key] <= current_time - TIME_WINDOW_SECONDS:
        click_logs[key].popleft()

    # Check if the number of clicks exceeds the max limit
    if len(click_logs[key]) >= MAX_CLICKS:
        print(f"Fraudulent activity detected from {ip_address} for campaign {campaign_id}")
        return True

    click_logs[key].append(current_time)
    print(f"Legitimate click recorded from {ip_address}")
    return False

This example provides a function to filter traffic based on suspicious user-agent strings. It checks if a user agent matches any patterns commonly associated with bots, scrapers, or other automated tools, which is a key part of distinguishing human traffic from non-human traffic.

# A list of known bot signatures
BOT_SIGNATURES = [
    "bot", "spider", "crawler", "headless", "scraping"
]

def is_user_agent_suspicious(user_agent_string):
    """Checks if a user agent string contains known bot signatures."""
    if not user_agent_string:
        return True # Empty user agents are suspicious

    ua_lower = user_agent_string.lower()
    for signature in BOT_SIGNATURES:
        if signature in ua_lower:
            print(f"Suspicious user agent detected: {user_agent_string}")
            return True
            
    print(f"User agent appears clean: {user_agent_string}")
    return False

Types of Campaign Tracking

• Parameter-Based Tracking – This is the most common type, using URL parameters like UTMs (utm_source, utm_medium, utm_campaign) to append tracking data to a link. It is essential for attributing clicks to specific campaigns and analyzing traffic sources for clear signs of fraud, like a high number of clicks from an irrelevant source.
• Pixel-Based Tracking – Involves placing a small, invisible pixel on a webpage or in an ad. When the pixel loads, it fires a request to a server, logging an impression or a click. In fraud detection, it helps verify that an ad was actually rendered and can be used to track user behavior post-click.
• Server-to-Server Tracking – This method, also known as postback tracking, sends data directly from one server to another without relying on the user's browser. It is more secure and reliable for fraud prevention because it is not susceptible to client-side manipulation, providing a trusted source for conversion and click data.
• Device Fingerprinting – This technique collects a set of attributes from a user's device (e.g., OS, browser version, screen resolution) to create a unique identifier. In campaign tracking, it helps identify and block users who try to mask their identity by changing IPs or clearing cookies, exposing sophisticated fraud patterns.

How Churn rate Works

Incoming Ad Traffic
        │
        ▼
+-----------------------+
│ Attribute Extraction  │
│ (IP, User Agent, ID)  │
+-----------------------+
        │
        ▼
+-----------------------+      +-------------------+
│  Churn Rate Analysis  ├─────▶│ Historical Data │
│ (Rate of Change)      │      +-------------------+
+-----------------------+
        │
        ▼
+-----------------------+
│ Fraud Decision Logic  │
│ (Thresholds, Rules)   │
+-----------------------+
        │
        └─▶ Flag as: [Legitimate] or [Suspicious/Bot]

🧠 Core Detection Logic

Example 1: IP Address Churn Detection

This logic identifies a single device or user cycling through numerous IP addresses in a short time, a common tactic for bots using proxy networks to appear as different users. It is a foundational rule in real-time traffic filtering.

// Rule: Flag users with high IP velocity
FUNCTION check_ip_churn(user_id, current_ip, time_window):
  // Get recent IPs for the user_id from a temporary cache
  recent_ips = GET_IPS_FOR_USER(user_id, time_window)

  // Add the current IP to the list
  ADD_IP_TO_HISTORY(user_id, current_ip)

  // Count unique IPs
  unique_ip_count = COUNT_UNIQUE(recent_ips)

  // Define the threshold
  IP_CHURN_THRESHOLD = 10

  // Check if the count exceeds the threshold
  IF unique_ip_count > IP_CHURN_THRESHOLD:
    RETURN "FLAG_AS_FRAUD"
  ELSE:
    RETURN "LEGITIMATE"

Example 2: User-Agent String Churn

Fraudsters often rotate user-agent strings along with IPs to make their bots harder to fingerprint. This logic detects sources that report different browser or device information from the same IP address, indicating an attempt to mask a bot’s identity.

// Rule: Flag IPs with a high diversity of User-Agents
FUNCTION check_ua_churn(ip_address, current_ua, time_window):
  // Get recent User-Agents for the IP
  recent_uas = GET_UAS_FOR_IP(ip_address, time_window)
  ADD_UA_TO_HISTORY(ip_address, current_ua)

  // Count unique User-Agents
  unique_ua_count = COUNT_UNIQUE(recent_uas)

  // Define the threshold
  UA_CHURN_THRESHOLD = 5

  IF unique_ua_count > UA_CHURN_THRESHOLD:
    // This IP is likely a gateway for a botnet
    RETURN "FLAG_AS_FRAUD"
  ELSE:
    RETURN "LEGITIMATE"

Example 3: Session Attribute Mismatch

This heuristic logic flags traffic where attributes that should be stable suddenly change mid-session. For example, a device fingerprint should not change from one click to the next within the same browsing session. This points to sophisticated bot behavior or session hijacking.

// Rule: Detect inconsistent identifiers within a single session
FUNCTION check_session_consistency(session_id, current_device_fingerprint):
  // Retrieve the initial fingerprint recorded for the session
  initial_fingerprint = GET_INITIAL_FINGERPRINT(session_id)

  IF initial_fingerprint IS NULL:
    // First event in session, store it
    STORE_INITIAL_FINGERPRINT(session_id, current_device_fingerprint)
    RETURN "LEGITIMATE"

  // Compare current fingerprint with the initial one
  IF current_device_fingerprint != initial_fingerprint:
    // Attributes have churned mid-session, which is highly suspicious
    RETURN "FLAG_AS_FRAUD"
  ELSE:
    RETURN "LEGITIMATE"

📈 Practical Use Cases for Businesses

• Campaign Shielding – Real-time blocking of clicks from sources exhibiting high churn rates preserves ad budgets by preventing payment for automated, non-human traffic.
• Data Integrity – By filtering out traffic with high attribute churn, businesses ensure their analytics platforms reflect genuine user engagement, leading to more accurate decision-making.
• ROAS Optimization – Eliminating fraudulent traffic sources improves the overall return on ad spend (ROAS) by ensuring that marketing funds are spent on audiences with a real potential for conversion.
• Lead Generation Filtering – For lead-gen campaigns, churn analysis can be used to disqualify form submissions from sources that show rapid changes in IP or device data, preventing fake leads from entering the sales funnel.

Example 1: Pre-Bid Ad Request Filtering

In programmatic advertising, this logic can be used to decide whether to bid on an ad impression. If the incoming request shows signs of high churn, the system declines to bid, saving money and avoiding low-quality placements.

// Logic for a Demand-Side Platform (DSP)
FUNCTION should_bid_on_request(ad_request):
  ip = ad_request.ip
  user_agent = ad_request.user_agent
  device_id = ad_request.device_id

  // Check for high IP churn associated with the device ID
  ip_churn_score = CALCULATE_IP_CHURN(device_id)

  // Check for high UA churn associated with the IP
  ua_churn_score = CALCULATE_UA_CHURN(ip)

  IF ip_churn_score > 0.8 OR ua_churn_score > 0.9:
    RETURN "DO_NOT_BID"
  ELSE:
    RETURN "BID"

Example 2: Post-Click Fraud Analysis

After a click occurs, this logic can run asynchronously to score its quality. If the click is deemed fraudulent due to high churn, its cost can be credited back, and the source can be added to a blocklist for future campaigns.

// Logic for a post-click analysis service
PROCEDURE analyze_click_quality(click_event):
  source_id = click_event.source_id
  ip = click_event.ip
  device_id = click_event.device_id
  timestamp = click_event.timestamp

  // Look at clicks from the same source_id in the last hour
  recent_clicks = GET_CLICKS(source_id, 1_HOUR)
  
  unique_ips = COUNT_DISTINCT(recent_clicks.map(c -> c.ip))
  unique_devices = COUNT_DISTINCT(recent_clicks.map(c -> c.device_id))

  // If one source generates clicks from too many IPs or devices
  IF unique_ips > 20 OR unique_devices > 10:
    MARK_AS_FRAUDULENT(click_event)
    ADD_TO_BLOCKLIST(source_id)

🐍 Python Code Examples

This function simulates checking for IP address churn. It maintains a dictionary to track the IPs used by each user ID and flags a user if the number of unique IPs exceeds a set threshold within a time window (not implemented for simplicity).

user_ip_history = {}
IP_CHURN_THRESHOLD = 5

def check_ip_churn(user_id, current_ip):
    """Flags a user if they churn through too many IPs."""
    if user_id not in user_ip_history:
        user_ip_history[user_id] = set()

    user_ip_history[user_id].add(current_ip)

    if len(user_ip_history[user_id]) > IP_CHURN_THRESHOLD:
        print(f"ALERT: High IP churn detected for user {user_id}.")
        return "FRAUDULENT"
    
    return "VALID"

# Simulation
check_ip_churn("user-123", "192.168.1.1")
check_ip_churn("user-123", "192.168.1.2")
check_ip_churn("user-123", "10.0.0.5")
check_ip_churn("user-123", "172.16.0.8")
check_ip_churn("user-123", "203.0.113.10")
check_ip_churn("user-123", "203.0.113.25") # This will trigger the alert

This example demonstrates how to detect user-agent churn from a single IP address. Bots often use one IP as a gateway and rotate through many user-agent strings to simulate different devices and browsers.

ip_ua_history = {}
UA_CHURN_THRESHOLD = 3

def check_ua_churn(ip_address, user_agent):
    """Flags an IP if it uses too many different User-Agents."""
    if ip_address not in ip_ua_history:
        ip_ua_history[ip_address] = set()

    ip_ua_history[ip_address].add(user_agent)

    if len(ip_ua_history[ip_address]) > UA_CHURN_THRESHOLD:
        print(f"ALERT: High User-Agent churn from IP {ip_address}.")
        return "SUSPICIOUS_IP"
        
    return "APPEARS_NORMAL"

# Simulation
ip = "198.51.100.5"
check_ua_churn(ip, "Mozilla/5.0 (Windows NT 10.0; Win64; x64) ...")
check_ua_churn(ip, "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) ...")
check_ua_churn(ip, "Mozilla/5.0 (iPhone; CPU iPhone OS 15_5 like Mac OS X) ...")
check_ua_churn(ip, "Mozilla/5.0 (Linux; Android 12; SM-G991U) ...") # This triggers the alert

Types of Churn rate

• IP Address Churn – This is the rate at which a single entity (identified by a device fingerprint or user ID) cycles through different IP addresses. High IP churn is a classic sign of proxy or VPN abuse used to generate seemingly unique visitors.
• User-Agent Churn – This measures how frequently a single IP address or device presents different user-agent strings. Bots rapidly change user agents to mimic a diverse range of devices and browsers, a pattern that legitimate users do not exhibit.
• Device ID Churn – In mobile advertising, this refers to the rapid cycling of resettable device identifiers (like Apple’s IDFA or Google’s GAID) from a single IP or device fingerprint. It is a key indicator of mobile ad fraud, often originating from device emulators in data centers.
• Geographic Churn – This type of churn tracks impossibly fast changes in a user’s geographic location as derived from their IP address. If a user appears in one country and then another minutes later, it signals the use of a geographically distributed proxy network.
• Behavioral Churn – This refers to abrupt and unnatural shifts in a user’s on-site behavior patterns. For example, a source that consistently produces clicks with a 1-second time-on-site suddenly switching to a 60-second time-on-site could indicate a bot script being reconfigured.

How Click Bots Works

Incoming Click → +--------------------------+ → Is it a Bot? → +---------------------+
                    | Traffic Security Gateway |                  | Heuristic Analysis  |
                    +--------------------------+                  | Behavioral Analysis |
                                │                                 | Signature Matching  |
                                │                                 +---------------------+
                                │                                           │
                                │                                           ↓
                                │                         +----------------+  +--------------+
                                └────────────────────────→|  Block Action  |  | Allow Action |
                                                          +----------------+  +--------------+

🧠 Core Detection Logic

Example 1: IP Reputation and Filtering

This logic checks the source IP address of a click against known blocklists containing IPs from data centers, VPNs, or proxies, which are commonly used for bot traffic. It’s a foundational layer of protection that filters out traffic from non-residential, high-risk network sources before performing more complex analysis.

FUNCTION check_ip_reputation(ip_address):
  IF ip_address IN known_datacenter_ips OR ip_address IN known_proxy_list:
    RETURN "fraudulent"
  ELSE:
    RETURN "legitimate"
END FUNCTION

Example 2: Click Timestamp Anomaly

This logic analyzes the timing and frequency of clicks originating from the same user or IP address. Clicks that occur at perfectly regular intervals or far too quickly for a human to perform are flagged as suspicious. This helps catch simple automated scripts that don’t randomize their behavior.

FUNCTION analyze_click_timing(user_id, click_timestamp):
  last_click_time = GET_LAST_CLICK_TIME(user_id)
  time_difference = click_timestamp - last_click_time

  IF time_difference < 1.0 SECONDS:
    INCREMENT_STRIKE_COUNT(user_id)
    RETURN "suspicious_too_fast"

  IF GET_STRIKE_COUNT(user_id) > 5:
    RETURN "fraudulent_high_frequency"

  RECORD_CLICK_TIME(user_id, click_timestamp)
  RETURN "legitimate"
END FUNCTION

Example 3: User-Agent Validation

This logic inspects the user-agent string sent by the browser. It flags traffic from outdated browsers, known bot user-agents, or user-agents that are inconsistent with other device signals (e.g., a mobile browser user-agent coming from a desktop IP range). This helps identify non-standard or spoofed client environments.

FUNCTION validate_user_agent(user_agent_string):
  IF user_agent_string IN known_bot_signatures:
    RETURN "fraudulent"
  
  IF is_headless_browser(user_agent_string):
    RETURN "fraudulent"
    
  IF NOT matches_standard_format(user_agent_string):
    RETURN "suspicious_malformed"
    
  RETURN "legitimate"
END FUNCTION

📈 Practical Use Cases for Businesses

• Campaign Shielding – Protects PPC campaign budgets by automatically identifying and blocking clicks from bots and competitors, ensuring that ad spend is used to reach genuine potential customers.
• Data Integrity – Ensures marketing analytics are clean and reliable by filtering out bot traffic that inflates click metrics and skews key performance indicators like click-through and conversion rates.
• ROAS Optimization – Improves Return on Ad Spend (ROAS) by preventing budget waste on fraudulent interactions, thereby increasing the proportion of the budget that drives real conversions and revenue.
• Affiliate Fraud Prevention – Deters fraudulent publishers in affiliate programs from using bots to generate fake clicks on their links to earn unmerited commissions.

Example 1: Geolocation Mismatch Rule

This pseudocode demonstrates a common rule used to protect campaigns that target specific geographic regions. It checks if the click’s IP-based location matches the campaign’s targeted country, blocking clicks from outside the intended area, a common sign of bot or click farm activity.

FUNCTION check_geo_targeting(click_ip, campaign_target_country):
  click_country = GET_COUNTRY_FROM_IP(click_ip)
  
  IF click_country != campaign_target_country:
    BLOCK_IP(click_ip)
    LOG_FRAUD_ATTEMPT("Geo Mismatch", click_ip, campaign_target_country)
    RETURN False
  ELSE:
    RETURN True
END FUNCTION

Example 2: Session Click Velocity Scoring

This logic scores a user session based on how many ads are clicked within a specific timeframe. A high score indicates robotic, non-human behavior and results in the session being flagged as fraudulent, which is useful for stopping more sophisticated bots that use the same session to attack multiple ads.

FUNCTION calculate_session_velocity_score(session_id, time_window_seconds):
  clicks = GET_CLICKS_IN_WINDOW(session_id, time_window_seconds)
  click_count = COUNT(clicks)
  
  // Assign a score based on click frequency
  IF click_count > 10:
    score = 100 // High-risk
  ELSE IF click_count > 5:
    score = 75 // Suspicious
  ELSE:
    score = 10 // Low-risk
    
  IF score >= 75:
    FLAG_SESSION_AS_FRAUD(session_id, score)
    
  RETURN score
END FUNCTION

🐍 Python Code Examples

This code defines a function to detect abnormally high click frequency from a single IP address. It tracks click timestamps and flags an IP as fraudulent if the number of clicks exceeds a set threshold within a minute, a common indicator of a simple click bot.

from collections import defaultdict
import time

click_log = defaultdict(list)
FRAUD_THRESHOLD = 15  # Max clicks per minute

def is_fraudulent_frequency(ip_address):
    current_time = time.time()
    # Filter out clicks older than 60 seconds
    click_log[ip_address] = [t for t in click_log[ip_address] if current_time - t < 60]
    
    # Add the new click
    click_log[ip_address].append(current_time)
    
    # Check if the click count exceeds the threshold
    if len(click_log[ip_address]) > FRAUD_THRESHOLD:
        print(f"Fraudulent activity detected from IP: {ip_address}")
        return True
        
    return False

# Simulation
is_fraudulent_frequency("192.168.1.10") # Returns False
for _ in range(20):
    is_fraudulent_frequency("192.168.1.11") # Will return True after 16th call

This example demonstrates how to filter traffic based on suspicious User-Agent strings. The function checks if a given user agent contains keywords commonly associated with automated scripts or headless browsers used by bots for ad fraud.

def is_suspicious_user_agent(user_agent):
    suspicious_keywords = ["bot", "headless", "phantomjs", "crawler", "python-requests"]
    
    # Normalize to lower case for case-insensitive matching
    ua_lower = user_agent.lower()
    
    for keyword in suspicious_keywords:
        if keyword in ua_lower:
            print(f"Suspicious user agent detected: {user_agent}")
            return True
            
    return False

# Simulation
user_agent_1 = "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36"
user_agent_2 = "python-requests/2.25.1"

is_suspicious_user_agent(user_agent_1) # Returns False
is_suspicious_user_agent(user_agent_2) # Returns True

Types of Click Bots

• Simple Script Bots
  These are the most basic type, often running from a single server or device. They repeatedly request web pages and click on ads without attempting to mimic human behavior, making them relatively easy to detect through IP analysis and frequency caps.
• Sophisticated Bots
  These advanced bots are programmed to imitate human-like actions, such as randomizing the time between clicks, moving the mouse cursor, and browsing other pages on a site. This makes them harder to distinguish from legitimate traffic without behavioral analysis.
• Botnets
  A botnet is a network of thousands or millions of infected devices (computers, smartphones) controlled by a fraudster. Because the clicks originate from a vast number of different residential IPs, botnets are effective at bypassing simple IP-based detection rules.
• Residential Proxy Bots
  This type of bot routes its traffic through residential proxy networks, which are pools of IP addresses belonging to real internet users. This technique makes the bot’s traffic appear as if it’s coming from genuine home users, making it highly effective at evading detection systems that block data center IPs.
• Click Injection Bots
  Primarily found in mobile environments, these bots are part of malicious apps that “inject” a click just before another app’s installation is complete. This allows the fraudulent app to illegitimately claim credit and receive the payout for the app install.

How Click farms Works

+----------------+      +---------------------+      +-----------------+
|   Advertiser   |----->|   Ad Network/Site   |----->|   User's Device |
+----------------+      +---------------------+      +-----------------+
                           |           ^
                           |           | Legitimate Traffic
                           v           |
+----------------------+   +-----------------------------+   +----------------------+
| Fraud Detection System|<- | Malicious Publisher/Network |<--|      Click Farm      |
| (Blocks & Reports)   |   +-----------------------------+   | (Humans or Bots)     |
+----------------------+      |           |                +----------------------+
                           └-----------└------> Illegitimate Clicks

🧠 Core Detection Logic

Example 1: IP Velocity and Reputation

This logic tracks the number of clicks originating from a single IP address or a range of related IPs over a short period. A sudden, high volume of clicks from one source is a strong indicator of a click farm or botnet. This fits into the network analysis layer of traffic protection.

FUNCTION checkIpVelocity(ip_address, time_window, threshold):
  click_count = count_clicks_from_ip(ip_address, time_window)
  ip_reputation = get_ip_reputation(ip_address) // e.g., known proxy, data center

  IF click_count > threshold OR ip_reputation == 'suspicious':
    RETURN 'FRAUDULENT'
  ELSE:
    RETURN 'VALID'

Example 2: Behavioral Analysis

This logic analyzes on-page user behavior to determine if it’s human-like. It measures metrics like mouse movement patterns, time spent on the page before clicking, and scroll depth. Bots often exhibit robotic, predictable movements, while click farm workers may click too quickly with no other interaction.

FUNCTION analyzeSessionBehavior(session_data):
  time_on_page = session_data.time_end - session_data.time_start
  has_mouse_moved = session_data.mouse_events > 5
  has_scrolled = session_data.scroll_depth > 0

  IF time_on_page < 2_SECONDS AND NOT has_mouse_moved:
    RETURN 'FRAUDULENT'
  
  IF click_event_happened AND time_on_page < 5_SECONDS AND NOT has_scrolled:
    RETURN 'HIGH_RISK'
  
  RETURN 'VALID'

Example 3: Geo and Device Mismatch

This logic cross-references the geographic location of the IP address with the user's device settings, such as language and timezone. A significant mismatch, like a click from a Vietnamese IP on a device set to US English and a US timezone, suggests the use of a VPN or proxy to hide the user's true origin.

FUNCTION checkGeoMismatch(ip_geo, device_language, device_timezone):
  // Assumes ip_geo is an object with country, timezone, etc.
  
  IF ip_geo.country != get_country_from_language(device_language):
    // Strong indicator if language and IP country don't align
    RETURN 'SUSPICIOUS'

  IF ip_geo.timezone != device_timezone:
    // Weaker indicator, but adds to the risk score
    RETURN 'SUSPICIOUS'
  
  RETURN 'VALID'

📈 Practical Use Cases for Businesses

• Campaign Shielding – Businesses use click farm detection to automatically block fraudulent IPs and devices from seeing their ads, preventing budget waste before a click even occurs and protecting PPC campaigns.
• Analytics Purification – By filtering out fake traffic, companies ensure their analytics platforms reflect true user engagement, leading to more accurate data for strategic decision-making and performance measurement.
• Conversion Funnel Protection – Detection logic is applied to lead-generation forms and checkout pages to prevent bots and fraudulent users from creating fake accounts or submitting spam, ensuring the sales team engages with genuine leads.
• Return on Ad Spend (ROAS) Optimization – By eliminating wasteful spending on fraudulent clicks, businesses can reallocate their budget to channels and campaigns that reach real customers, directly improving their overall return on ad spend.

Example 1: Geofencing Rule

A business targeting customers only in the UK can use geofencing to automatically flag any click originating from outside its target area, a common tactic for click farms in different countries.

RULE Geofence_UK:
  WHEN click.ip_geolocation.country NOT IN ('GB')
  THEN 
    FLAG 'fraud'
    ACTION block_ip

Example 2: Session Engagement Scoring

An e-commerce site can score sessions based on engagement. Clicks from sessions with zero scroll activity and a sub-three-second page view time are flagged as fraudulent, typical of automated scripts.

RULE Engagement_Score:
  DEFINE
    low_engagement = session.scroll_depth == 0 AND session.time_on_page < 3
  WHEN low_engagement IS TRUE
  THEN
    SCORE session.fraud_score + 50
    IF session.fraud_score > 75 THEN
      ACTION flag_and_review

🐍 Python Code Examples

This code identifies high-frequency clicking from a single IP address within a specific time frame, a common sign of a click farm. It helps block IPs that exhibit bot-like, repetitive behavior.

# Example 1: Detect Abnormal Click Frequency
from collections import defaultdict
import time

clicks = defaultdict(list)
FRAUD_THRESHOLD = 10  # Clicks
TIME_WINDOW = 60  # Seconds

def is_fraudulent_ip(ip_address):
    current_time = time.time()
    # Remove clicks older than the time window
    clicks[ip_address] = [t for t in clicks[ip_address] if current_time - t < TIME_WINDOW]
    
    clicks[ip_address].append(current_time)
    
    if len(clicks[ip_address]) > FRAUD_THRESHOLD:
        print(f"Fraudulent activity detected from IP: {ip_address}")
        return True
    return False

# Simulation
is_fraudulent_ip("192.168.1.101") # Returns False
for _ in range(15):
    is_fraudulent_ip("192.168.1.102") # Will eventually return True

This script analyzes user-agent strings to filter out known bot signatures or suspicious patterns. A real user's browser provides a standard user-agent, while bots may use outdated, strange, or generic ones.

# Example 2: Filter Suspicious User Agents
def is_suspicious_user_agent(user_agent):
    suspicious_keywords = ["bot", "spider", "headless", "scraping"]
    user_agent_lower = user_agent.lower()
    
    if not user_agent:
        return True # Empty user agent is highly suspicious
        
    for keyword in suspicious_keywords:
        if keyword in user_agent_lower:
            print(f"Suspicious user agent detected: {user_agent}")
            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("GoogleBot/2.1") # True
is_suspicious_user_agent("") # True

Types of Click farms

• Manual Click Farms – These operations employ large groups of low-paid human workers who manually click on ads, follow accounts, or post comments. Because a human is performing the action, this type can be harder to detect than purely automated bots.
• Bot-Powered Click Farms – These use automated scripts and botnets (networks of infected computers) to generate clicks and other engagement at a massive scale. They are faster and cheaper to operate than manual farms but can be easier to identify through behavioral analysis.
• Mobile Device Farms – These are physical locations containing hundreds or thousands of real mobile phones arranged on racks, used to generate fraudulent app installs and mobile ad clicks. They use real devices to better mimic legitimate user profiles and bypass emulators detectors.
• Hybrid Click Farms – This model combines automation with human intervention. For instance, bots might perform the initial browsing and clicking, while human workers are used to solve CAPTCHAs or complete complex sign-up forms that bots cannot handle, making them highly evasive.