In app events

How In app events Works

[User Action in App] → [SDK Records Event] → [Data Sent to Server] → [+ Fraud Analysis +] → [Attribution Decision]
      │                     │                       │                      │                       └─ Legitimate: Attribute Install
      │                     │                       │                      │
      └─────────────────────┴───────────────────────┴──────────────────────┴─ Fraudulent: Block & Report

The process of using in-app events for fraud detection is a multi-layered system that transforms user interactions into actionable security insights. It begins the moment a user interacts with an application and concludes with a decision on whether the traffic source is legitimate or fraudulent. This pipeline ensures that advertisers are paying for genuine user engagement, not automated or fake activity.

Event Tracking and Data Collection

When a user performs an action within an app, like completing a tutorial or adding an item to a cart, the app’s integrated Software Development Kit (SDK) records this specific interaction as an in-app event. This event data, which includes the event type and a timestamp, forms the basic building block for all subsequent analysis. This initial step is critical for capturing the raw behavioral data needed to understand the user journey.

Data Transmission and Aggregation

Once an event is recorded by the SDK, it is securely transmitted to a central server or an analytics platform. Here, events from countless users and devices are aggregated. This centralized data hub allows for the analysis of patterns at a macro level, connecting post-install activities back to the initial ad click and install source. This aggregation is essential for building a comprehensive view of traffic quality from different advertising partners.

Fraud Detection and Analysis

In this crucial stage, the aggregated event data is scrutinized by advanced algorithms and machine learning models. The analysis looks for anomalies that signal non-human or fraudulent behavior, such as an impossibly short time between installing an app and making a purchase, or a high volume of installs from one source with zero subsequent user engagement. This is where raw data is turned into fraud intelligence.

Diagram Breakdown

[User Action in App]

This is the starting point, representing any meaningful interaction a user has with the application after installing it. Examples include logging in, reaching a new level, or making a purchase. The authenticity and sequence of these actions are fundamental to detecting fraud.

[SDK Records Event]

The application’s embedded SDK acts as a listener, capturing the user action and converting it into a structured data point. It ensures that user behavior is accurately recorded for transmission and analysis.

[Data Sent to Server]

The recorded event data is sent from the user’s device to a remote server. This step centralizes data from all users, making it possible to perform large-scale analysis and identify widespread fraud patterns that wouldn’t be visible at an individual level.

[+ Fraud Analysis +]

This is the core of the detection process. The server-side system applies a series of checks and behavioral models to the event data to score its legitimacy. It compares event timing, sequences, and frequency against established benchmarks of normal human behavior.

[Attribution Decision]

Based on the fraud analysis, a final decision is made. If the in-app event patterns appear legitimate, the install is attributed to the advertising source, and the partner is credited. If the patterns are flagged as fraudulent, the install and associated events are blocked, and the advertiser avoids paying for fake traffic.

🧠 Core Detection Logic

Example 1: Event Timing Anomaly Detection

This logic identifies fraud by measuring the time between consecutive in-app events. Bots often trigger events at a speed no human could achieve. Detecting these impossibly short timeframes helps filter out automated traffic and protect attribution data.

FUNCTION check_event_timing(events):
  SORT events BY timestamp

  FOR i FROM 1 TO length(events):
    time_diff = events[i].timestamp - events[i-1].timestamp
    
    // Check time between install and first action
    IF events[i-1].name == "install" AND events[i].name == "purchase":
      IF time_diff < 10 SECONDS:
        RETURN "Fraud: Implausible install-to-purchase time."

    // Check time between sequential game levels
    IF events[i-1].name == "level_1_complete" AND events[i].name == "level_2_complete":
      IF time_diff < 5 SECONDS:
        RETURN "Fraud: Unnaturally fast level completion."
        
  RETURN "Legitimate"

Example 2: Behavioral Sequence Validation

This logic validates that in-app events occur in a logical order. Real users follow predictable paths (e.g., adding an item to a cart before purchasing). Bots may skip steps, and this rule catches illogical event sequences that expose non-human behavior.

FUNCTION validate_event_sequence(user_events):
  has_added_to_cart = FALSE
  has_purchased = FALSE

  FOR event IN user_events:
    IF event.name == "add_to_cart":
      has_added_to_cart = TRUE
    IF event.name == "purchase":
      has_purchased = TRUE
      IF has_added_to_cart == FALSE:
        RETURN "Fraud: Purchase event occurred without add_to_cart event."
  
  RETURN "Legitimate"

Example 3: Engagement Ratio Analysis

This logic assesses the quality of traffic from a specific source by comparing the number of installs to the number of meaningful post-install events. A high number of installs with almost no subsequent engagement is a strong indicator of an install farm or bot traffic.

FUNCTION analyze_publisher_quality(publisher_id):
  installs = get_installs_for_publisher(publisher_id)
  key_events = get_key_events_for_publisher(publisher_id) // e.g., level_complete, purchase

  IF count(installs) > 1000 AND count(key_events) == 0:
    RETURN "Fraud: High install volume with zero user engagement."
  
  engagement_ratio = count(key_events) / count(installs)
  
  IF engagement_ratio < 0.01: // Threshold for low engagement
    RETURN "Suspicious: Extremely low engagement ratio."

  RETURN "Legitimate"

📈 Practical Use Cases for Businesses

• Campaign Shielding – Reallocating advertising budgets away from fraudulent publishers and channels that deliver fake installs with no real user activity, thereby maximizing ROI.
• ROAS Protection – Ensuring that Return on Ad Spend (ROAS) calculations are based on genuine revenue-generating events, like real purchases, not faked actions from bots.
• Analytics Integrity – Keeping user analytics data clean from the noise of fake events, which allows for accurate product and marketing decisions based on real user behavior.
• Publisher Vetting – Identifying and blocking publishers who consistently send low-quality traffic characterized by high install counts but a complete lack of post-install engagement.

Example 1: New User Onboarding Funnel

This logic ensures that new users follow a plausible sequence of events during their first session. It helps catch bots that trigger a registration or purchase event immediately after install without completing the necessary intermediate steps like a tutorial.

FUNCTION check_new_user_funnel(user_id):
  events = get_events_for_user(user_id)
  
  install_time = find_event_timestamp(events, "install")
  tutorial_complete_time = find_event_timestamp(events, "tutorial_complete")
  registration_time = find_event_timestamp(events, "register")

  IF registration_time AND install_time:
    // Registration must happen after install
    IF registration_time < install_time:
      RETURN "Fraud: Registration before install."

    // Registration should follow tutorial
    IF registration_time < tutorial_complete_time:
      RETURN "Fraud: Registration before tutorial completion."
      
  RETURN "Legitimate"

Example 2: Purchase Value Anomaly Detection

This logic flags sources that generate an unusually high number of high-value purchase events. This is a common pattern in CPA fraud, where fraudsters aim to maximize payouts by faking the most valuable actions.

FUNCTION check_purchase_anomalies(publisher_id):
  purchases = get_purchase_events(publisher_id)
  
  high_value_purchases = 0
  FOR purchase IN purchases:
    IF purchase.value > 100: // Define high-value threshold
      high_value_purchases += 1
      
  total_purchases = count(purchases)
  
  IF total_purchases > 50:
    high_value_ratio = high_value_purchases / total_purchases
    IF high_value_ratio > 0.90:
      RETURN "Fraud: Suspiciously high ratio of high-value purchases."
      
  RETURN "Legitimate"

🐍 Python Code Examples

This Python code demonstrates how to calculate the Click-to-Install Time (CTIT) from a list of user events. Abnormally short CTIT values are a strong indicator of click injection fraud, where a fraudulent click is fired just before an install is completed to steal attribution.

import datetime

def analyze_ctit(events):
    """Analyzes click-to-install time to detect fraud."""
    click_time, install_time = None, None
    for event in events:
        if event['name'] == 'click':
            click_time = datetime.datetime.fromisoformat(event['timestamp'])
        if event['name'] == 'install':
            install_time = datetime.datetime.fromisoformat(event['timestamp'])

    if click_time and install_time:
        ctit = (install_time - click_time).total_seconds()
        if ctit < 10:  # A threshold of less than 10 seconds is highly suspicious
            print(f"Fraud detected: CTIT is suspiciously low at {ctit} seconds.")
            return False
    print("No fraud detected based on CTIT.")
    return True

# Example Data
user_events = [
    {'name': 'click', 'timestamp': '2023-10-27T10:00:00'},
    {'name': 'install', 'timestamp': '2023-10-27T10:00:05'}
]
analyze_ctit(user_events)

This example simulates checking for event frequency anomalies. It identifies users who generate an excessive number of events in a short period, a pattern that is characteristic of bot activity rather than genuine human interaction.

from collections import defaultdict

def check_event_frequency(event_stream, time_window_seconds=300, max_events=50):
    """Flags users with abnormal event frequency."""
    user_event_counts = defaultdict(int)
    
    for event in event_stream:
        user_id = event['user_id']
        user_event_counts[user_id] += 1

    for user, count in user_event_counts.items():
        if count > max_events:
            print(f"Fraud alert: User {user} generated {count} events in {time_window_seconds}s.")

# Example Data (simulating a stream of events over 5 minutes)
event_stream = [
    {'user_id': 'user_A', 'event': 'login'},
    {'user_id': 'user_B', 'event': 'level_up'},
    # ... imagine user_B has 99 more events here
]
# To simulate a full stream for user_B
for _ in range(99):
    event_stream.append({'user_id': 'user_B', 'event': 'action'})

check_event_frequency(event_stream, max_events=100)

Types of In app events

• Standard Events – These are common, predefined actions tracked across most apps, such as 'registration', 'login', or 'purchase'. Their standardized nature allows for easy, cross-platform analysis and benchmarking, providing a foundational layer for detecting broad, non-sophisticated fraud patterns.
• Custom Events – These are unique actions defined by the app developer, specific to the app's functionality, like 'character_upgrade' in a game or 'playlist_created' in a music app. They offer granular behavioral insights, making it harder for bots to mimic the full range of authentic user engagement.
• Revenue Events – A critical subcategory of events that includes a monetary value, such as 'af_revenue' for an in-app purchase. These are prime targets for fraudsters, and validating them is crucial for protecting against CPA fraud and ensuring accurate ROI calculations.
• Engagement Events – These actions signify active use but may not have direct monetary value, such as 'tutorial_complete' or 'level_achieved'. Tracking these helps build a behavioral profile to differentiate between genuinely engaged users and fraudulent installs that show no post-install activity.
• Sentinel Events – These are non-visible or dummy events implemented specifically as fraud traps. A real user would never trigger them, so any activity on these events is an immediate and definitive signal of a bot or a malicious actor interacting with the app's code.

🛡️ Common Detection Techniques

• Behavioral Anomaly Detection - This technique involves establishing a baseline for normal user behavior and then identifying deviations. It detects fraud by spotting patterns that don't align with typical human interaction, such as impossibly fast event sequences or illogical user journeys.
• Click-to-Install Time (CTIT) Analysis - This measures the duration between an ad click and the first app open. Unusually short times (a few seconds) indicate click injection, while extremely long times can signal click flooding, making it a vital technique for catching attribution fraud.
• Event Funnel Analysis - This method maps out the logical sequence of events a real user should follow, such as 'add_to_cart' before 'purchase'. Fraud is detected when bots skip essential steps in this funnel, revealing their non-human and illegitimate nature.
• Post-Install Activity Monitoring - This involves tracking user actions after an app is installed to check for engagement. A high volume of installs from a single source followed by zero in-app activity is a strong indicator of fraudulent installs generated by bots or device farms.
• IP and Device Fingerprinting - This technique correlates in-app events with the IP addresses and device characteristics that generate them. It helps identify fraud by detecting large numbers of "unique" user events originating from a single device or a suspicious group of IPs, which is a classic sign of a botnet.

🧰 Popular Tools & Services

Tool	Description	Pros	Cons
AppsFlyer (Protect360)	A widely-used mobile attribution and marketing analytics platform with a dedicated fraud protection suite. It uses a massive data scale to identify and block ad fraud in real-time and post-attribution.	Large device database, real-time and post-attribution detection, detailed fraud reports, strong AI/ML capabilities.	Can be expensive for smaller businesses, complexity may require dedicated expertise.
Adjust (Fraud Prevention Suite)	An analytics platform offering a proactive fraud prevention toolset that focuses on rejecting fraudulent traffic in real-time before it contaminates data. It detects bots, click spam, and SDK spoofing.	Real-time filtering, strong against common fraud types, offers anonymous IP filtering and distribution modeling.	Focus is more on real-time rejection, which may miss some sophisticated post-install fraud. UI can have delays.
TrafficGuard	A specialized ad fraud prevention service that offers real-time click-level protection and post-install analysis. It integrates with MMPs to validate attribution and block fake installs and engagements.	Full-funnel protection, transparent reporting, real-time blocking, complements MMPs well for enhanced security.	Requires integration with other platforms; primarily focused on fraud rather than being an all-in-one analytics suite.
mFilterIt	A full-funnel ad fraud detection and prevention solution that analyzes impressions, clicks, installs, and post-install events to provide holistic campaign protection against sophisticated fraud.	Covers every stage of the marketing funnel, validates in-app behavior beyond just installs, good for affiliate fraud detection.	As a specialized tool, it adds another layer to the tech stack rather than being a single platform for all analytics.

📊 KPI & Metrics

Tracking key performance indicators (KPIs) is essential to measure the effectiveness and financial impact of an in-app event-based fraud detection strategy. Monitoring these metrics helps quantify the accuracy of the detection engine, its impact on user acquisition costs, and the overall return on investment of the fraud prevention efforts.

Metric Name	Description	Business Relevance
Fraud Rate	The percentage of installs or in-app events flagged as fraudulent out of the total volume.	Provides a high-level view of the overall fraud problem and the effectiveness of filtering efforts.
False Positive Rate	The percentage of legitimate transactions that were incorrectly flagged as fraudulent.	A critical metric for ensuring that fraud filters are not blocking real users and potential revenue.
Install-to-Action Rate	The percentage of users who perform a key in-app event (e.g., purchase, registration) after installing.	Helps measure traffic quality from different sources and identify partners delivering non-engaged, likely fraudulent users.
Cost Per Action (CPA) Reduction	The decrease in cost for acquiring a genuinely engaged user after implementing fraud filters.	Directly measures the ROI of the fraud prevention system by showing how much money is saved on acquiring real customers.
Recall Rate	The percentage of all fraudulent transactions that the system successfully detected and blocked.	Indicates the accuracy and comprehensiveness of the detection models in catching known and unknown fraud types.

These metrics are typically monitored through real-time dashboards and detailed raw data reports provided by fraud prevention platforms. Feedback from these KPIs is used to continuously fine-tune detection rules and algorithms, adapting to new fraud tactics and optimizing the balance between blocking malicious activity and allowing legitimate users to proceed without friction.

🆚 Comparison with Other Detection Methods

Accuracy and Granularity

In-app event analysis offers far greater accuracy and granularity than simpler methods like IP blacklisting. While blacklisting can block known bad actors, it is a blunt instrument that can lead to high false positives. Analyzing in-app events provides deep behavioral context, allowing systems to detect sophisticated bots that use clean IPs but fail to mimic human engagement patterns. This method catches fraud that other systems miss by focusing on user quality, not just traffic source.

Real-Time vs. Post-Attribution Detection

Unlike real-time detection methods such as signature-based filtering, which block threats before a click is even registered, in-app event analysis often functions as a post-attribution or near-real-time system. It validates the quality of an install after it has occurred by monitoring subsequent behavior. While this means some initial fraudulent conversions may be recorded, it is highly effective at identifying advanced fraud that is designed to bypass initial checks. Many modern systems use a hybrid approach, combining real-time blocking with post-install analysis for comprehensive protection.

Effectiveness Against Sophisticated Fraud

Compared to CAPTCHAs or basic device fingerprinting, in-app event analysis is significantly more effective against modern, sophisticated fraud. Fraudsters can program bots to solve simple CAPTCHAs and can easily spoof device parameters. However, faking a logical and naturally timed sequence of post-install events (e.g., completing a tutorial, browsing multiple items, then making a purchase) is far more complex and costly for a fraudster to scale, making event analysis a robust defense layer.

⚠️ Limitations & Drawbacks

While powerful, relying solely on in-app event analysis for fraud detection has several limitations. Its effectiveness can be constrained by the sophistication of fraud, data privacy regulations, and implementation complexity, making it just one part of a comprehensive security strategy.

• Latency in Detection – Analysis is often post-attribution, meaning fraudulent installs may be detected after an advertiser has already been charged, requiring a refund process.
• Sophisticated Bot Mimicry – Advanced bots can be programmed to mimic human-like in-app event sequences, making them harder to distinguish from legitimate users.
• Data Volume and Cost – Tracking and processing billions of in-app events requires significant server resources and can be expensive, especially for apps with large user bases.
• Privacy Restrictions – Increasing privacy regulations (like Apple's App Tracking Transparency framework) can limit the amount and granularity of user-level data available for analysis, potentially reducing detection accuracy.
• Implementation Errors – The effectiveness of this method depends entirely on the correct implementation of SDKs and the accurate definition of events, which can be prone to human error.
• Limited View of Pre-Install Fraud – This method is focused on post-install activity and is less effective at detecting fraud that occurs at the impression or click level, such as click flooding.

In scenarios where real-time blocking is critical or where sophisticated bots are prevalent, a hybrid approach that combines pre-bid analysis, IP filtering, and post-install event validation is often more suitable.

❓ Frequently Asked Questions

How do in-app events differ from ad clicks for fraud detection?

Ad clicks are top-of-funnel actions that are relatively easy and cheap for fraudsters to fake at scale. In-app events, however, are actions that occur after an install, such as completing a level or making a purchase. These are much harder to mimic authentically, providing a more reliable signal of genuine user engagement and value.

Can bots fake in-app events?

Yes, sophisticated bots can trigger in-app events to appear legitimate. However, they often fail to replicate the complex timing, sequence, and behavioral nuances of real human interaction. Fraud detection systems capitalize on these anomalies, such as impossibly fast actions or illogical event flows, to identify and block bot-driven activity.

Is tracking in-app events compliant with privacy laws like GDPR and ATT?

Yes, but it requires careful implementation. Compliance with regulations like GDPR and Apple's App Tracking Transparency (ATT) framework mandates obtaining user consent before tracking their data. Many fraud detection systems rely on aggregated or anonymized data to identify broad patterns without compromising individual user privacy, ensuring they can operate effectively within these legal frameworks.

What is the most important in-app event to track for fraud detection?

There isn't a single most important event; rather, the combination of several events provides the strongest signal. A good strategy involves tracking both early engagement events (e.g., 'tutorial_complete') to ensure a user is real, and high-value conversion events (e.g., 'purchase') to protect revenue. Analyzing the entire user journey provides the most robust defense.

Do I need a third-party tool to analyze in-app events for fraud?

While a basic in-house analysis is possible, it is highly recommended to use a specialized third-party tool. Platforms like AppsFlyer or Adjust have access to vast datasets from billions of devices, allowing their machine learning models to identify new fraud patterns much faster and more accurately than an in-house system could.

🧾 Summary

In-app events are user actions tracked after an app install, serving as a critical tool in digital advertising fraud prevention. By analyzing the sequence and timing of these post-install behaviors—like registrations or purchases—advertisers can distinguish genuine users from bots. This method is vital for detecting sophisticated fraud, protecting ad budgets from being wasted on fake traffic, and ensuring campaign data remains accurate and reliable.

🔗 Related Articles

• The marketer's field guide to mobile ad fraud - https://www.appsflyer.com/resources/reports/mobile-ad-fraud-guide/
• Event Fraud In Mobile Apps: What is It & How To Combat | mFaaS - https://mfaas.com/blog/in-app-event-fraud/
• Full-Funnel App Fraud Protection: How mFilterIt Secures Campaigns - https://www.mfilterit.com/blog/full-funnel-app-fraud-protection-how-mfilterit-secures-campaigns/
• Mobile Ad Fraud Prevention | TrafficGuard - https://www.trafficguard.ai/solutions/mobile-ad-fraud-prevention
• How to Identify and Prevent Mobile Ad Fraud - MyTracker - https://mytracker.io/blog/how-to-identify-and-prevent-mobile-ad-fraud