AI-Powered Analytics

How AIPowered Analytics Works

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

🧠 Core Detection Logic

Example 1: Session Velocity Scoring

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

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

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

Example 2: Geographic Mismatch Detection

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

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

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

  RETURN "LEGITIMATE"

Example 3: Bot-Like User-Agent Filtering

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 Bots often use outdated, generic, or known non-standard UA strings.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

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

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

  RETURN "LEGITIMATE"

📈 Practical Use Cases for Businesses

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

Example 1: Geofencing Rule

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

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

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

Example 2: Session Authenticity Scoring

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

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

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

🐍 Python Code Examples

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

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

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

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

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

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

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

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

Types of AIPowered Analytics

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