Hybrid app

How Hybrid app Works

Incoming Ad Click → [+ Layer 1: Rules Engine] → [+ Layer 2: Behavioral Scan] → [+ Layer 3: Anomaly Detection] → Final Decision
        │                      │                          │                            │
        │                      └─ (Block known bad IPs)    │                            │
        │                                                 └─ (Analyze mouse movement)   │
        │                                                                              └─ (Score deviations)
        │
        └───────────────────────────────────────────────────────────────────────────────────→ [Valid/Invalid]

🧠 Core Detection Logic

Example 1: IP-Based Threat Intelligence

This logic checks an incoming click’s IP address against a known blacklist of fraudulent sources. It serves as a first line of defense, quickly eliminating traffic from data centers, proxies, and botnets before it consumes more advanced analytical resources. This is a fundamental component of rule-based filtering.

FUNCTION check_ip(click_event):
  ip_address = click_event.ip
  blacklist = get_threat_blacklist()

  IF ip_address IN blacklist:
    RETURN "invalid_traffic"
  ELSE:
    RETURN "needs_further_analysis"
END FUNCTION

Example 2: Session Click Frequency Analysis

This heuristic logic analyzes user behavior by tracking how many times a single user (identified by a session ID or device fingerprint) clicks an ad within a specific time window. Unnaturally high click frequency is a strong indicator of bot activity, as humans do not typically click the same ad repeatedly in seconds.

FUNCTION analyze_click_frequency(session_id, click_timestamp):
  // Retrieve past clicks for this session
  session_clicks = get_clicks_for_session(session_id, last_60_seconds)

  // Add current click to the list
  ADD click_timestamp to session_clicks

  // Check if count exceeds threshold
  IF count(session_clicks) > 5:
    RETURN "suspicious_frequency"
  ELSE:
    RETURN "normal_frequency"
END FUNCTION

Example 3: Geo-Mismatch Detection

This contextual logic compares the declared timezone of the user’s browser/device with the geographical location inferred from their IP address. A significant mismatch can indicate the use of a VPN or proxy to spoof location, a common tactic in ad fraud to target high-value geographic campaigns illegitimately.

FUNCTION check_geo_mismatch(click_event):
  ip_geo_country = get_country_from_ip(click_event.ip)
  browser_timezone = click_event.device.timezone

  // Get expected timezones for the IP's country
  expected_timezones = get_timezones_for_country(ip_geo_country)

  IF browser_timezone NOT IN expected_timezones:
    RETURN "geo_mismatch_detected"
  ELSE:
    RETURN "geo_consistent"
END FUNCTION

📈 Practical Use Cases for Businesses

• Campaign Shielding – A hybrid app automatically blocks invalid clicks from bots and competitors in real time. This directly protects PPC campaign budgets from being wasted on traffic that will never convert, ensuring ad spend is allocated toward reaching genuine customers.
• Data Integrity for Analytics – By filtering out bot traffic before it pollutes analytics platforms, businesses can trust their data. This leads to accurate insights into key metrics like click-through rates and user engagement, enabling better strategic decision-making and optimization.
• Lead Generation Funnel Protection – For businesses relying on lead forms, a hybrid approach ensures that submissions are from legitimate human users. It filters out bot-generated spam and fake sign-ups, improving the quality of sales leads and saving time for the sales team.
• Return on Ad Spend (ROAS) Improvement – By eliminating fraudulent ad interactions that drain budgets and skew performance data, a hybrid system directly contributes to a higher ROAS. Advertisers pay only for clicks with the potential for genuine engagement, maximizing the return on their investment.

Example 1: Time-Between-Events Rule

This logic prevents bots from executing actions faster than a human possibly could, such as clicking a button fractions of a second after a page loads.

FUNCTION check_action_timing(page_load_time, click_time):
  // Calculate time elapsed in seconds
  time_elapsed = click_time - page_load_time

  // Set minimum humanly possible time
  MIN_THRESHOLD = 0.5 // seconds

  IF time_elapsed < MIN_THRESHOLD:
    RETURN "Block: Action too fast, likely bot"
  ELSE:
    RETURN "Allow: Human-like speed"
END IF

Example 2: Session Authenticity Scoring

This pseudocode demonstrates scoring a session based on multiple signals. A hybrid system combines these scores to make a final decision, providing a more nuanced judgment than a single rule.

FUNCTION score_session(session_data):
  score = 0

  IF session_data.source is "Known Good Publisher":
    score = score + 20
  IF session_data.ip_type is "Data Center":
    score = score - 50
  IF session_data.has_mouse_events:
    score = score + 30
  IF session_data.click_frequency > 10 per minute:
    score = score - 40

  // Decision based on final score
  IF score < 0:
    RETURN "Invalid"
  ELSE:
    RETURN "Valid"
END IF

🐍 Python Code Examples

This function simulates checking a click's IP address against a predefined set of suspicious network types, such as data centers or public proxies. This helps filter out non-human traffic sources common in bot-driven fraud.

# A set of known fraudulent Autonomous System Numbers (ASNs)
FRAUDULENT_ASNS = {'ASN12345', 'ASN67890'}

def filter_by_asn(click_ip):
    """Flags an IP if it belongs to a known fraudulent ASN."""
    click_asn = get_asn_for_ip(click_ip) # Placeholder for an IP-to-ASN lookup service
    if click_asn in FRAUDULENT_ASNS:
        print(f"Blocking {click_ip}: Belongs to fraudulent ASN {click_asn}")
        return False
    return True

# Example for a real IP lookup would require a service like MaxMind
def get_asn_for_ip(ip):
    # This is a mock function. In a real scenario, you'd use a geoIP database.
    if ip.startswith("52.20."):
        return "ASN12345" # Example ASN for a data center
    return "ASN_NORMAL"

# --- Simulation ---
filter_by_asn("52.20.15.10") # Returns False
filter_by_asn("8.8.8.8")      # Returns True

This example demonstrates how to detect abnormally frequent clicks from a single user ID within a short time frame. Such rapid-fire activity is a strong indicator of an automated script or bot rather than genuine user interest.

from collections import defaultdict
import time

# Store click timestamps for each user ID
user_clicks = defaultdict(list)
CLICK_LIMIT = 5 # Max clicks
TIME_WINDOW = 10 # Within 10 seconds

def is_click_flood(user_id):
    """Checks if a user has clicked too frequently."""
    current_time = time.time()
    # Remove timestamps older than the time window
    user_clicks[user_id] = [t for t in user_clicks[user_id] if current_time - t < TIME_WINDOW]

    # Add the new click
    user_clicks[user_id].append(current_time)

    # Check the count
    if len(user_clicks[user_id]) > CLICK_LIMIT:
        print(f"Click flood detected for user {user_id}")
        return True
    return False

# --- Simulation ---
for i in range(6):
    is_click_flood("user-123")
    time.sleep(1)

Types of Hybrid app

• Layered Hybrid Model – This model processes traffic through a sequence of filters, starting with the fastest, low-cost checks (like IP blacklisting) and progressing to more resource-intensive analysis (like behavioral modeling). It efficiently removes obvious bots early, saving computational power for more sophisticated threats.
• Ensemble Hybrid Model – This approach uses multiple detection algorithms in parallel and combines their outputs to reach a final decision, often through a voting or weighting system. It increases accuracy by leveraging the diverse strengths of different models (e.g., combining a random forest with a neural network).
• Human-in-the-Loop Model – This type combines automated detection systems with manual review by human fraud analysts. The system flags ambiguous or high-risk traffic for an expert to examine, which helps reduce false positives and train the automated models with verified data, improving future accuracy.
• Adaptive Hybrid Model – This model uses machine learning to continuously adjust its own rules and parameters based on newly identified fraud patterns. It automatically learns from the traffic it analyzes, allowing the system to adapt to evolving bot tactics without needing constant manual reprogramming.