In game purchase

How In game purchase Works

+----------------+      +--------------------+      +------------------+      +-------------------+      +-------------------+
|   Fraud Bot    |----->| Fake In-Game Actions|----->| Trigger Ad Event |----->|  Simulate Click/  |----->|   Ad Network      |
| (Emulator/Script)|      | (e.g., Level Up)   |      | (View/Install)   |      |  In-App Purchase  |      |  (Records Invalid Data) |
+----------------+      +--------------------+      +------------------+      +-------------------+      +-------------------+
        │                      │                             │                            │                      │
        └──────────────────────┴─────────────┬─────────────┴────────────────────────────┴──────────────────────┘
                                             │
                               +-----------------------------+
                               | Traffic Security System     |
                               | (Analyzes & Flags Anomaly) |
                               +-----------------------------+

In the context of ad fraud, “in-game purchase” exploitation refers to a sophisticated scheme where criminals use automated scripts or bots to mimic legitimate player behavior. These bots perform actions within a mobile game specifically to trigger ad-related events, such as viewing a video ad to receive a reward or simulating an in-app purchase that an advertiser pays for on a cost-per-action basis. The goal is to generate fraudulent revenue for the bot operator at the expense of the advertiser’s budget. This process undermines the integrity of ad campaign data and results in significant financial losses.

Automated User Simulation

Fraudsters deploy bots, often running on emulators or compromised devices, that are programmed to perform a sequence of actions that a real player might take. This can include completing tutorial levels, collecting daily rewards, or reaching certain milestones. These actions are not for enjoyment but are the necessary steps to unlock and interact with rewarded ads or other monetized events within the game, making the fraudulent traffic appear legitimate at a surface level.

Ad Trigger Exploitation

Once the bot completes a prerequisite action, it triggers the display of an ad. The fraud continues as the bot simulates an interaction with that ad—a click, an app install, or even a purchase. For example, in cost-per-install (CPI) campaigns, bots can generate thousands of fake installs, leading advertisers to pay for users who don’t exist. These fraudulent events are then reported back to the ad network as legitimate conversions, siphoning money from the advertiser’s budget.

Data Obfuscation and Reporting

To avoid detection, these bot-driven actions are often spread across many different device IDs, IP addresses, and simulated locations. The fraudulent data, now mixed with legitimate user data, is sent to the ad network, which pays the “publisher” (the fraudster controlling the app where the ad was shown). This skews analytics, making campaigns appear more successful than they are and tricking advertisers into investing more into fraudulent channels.

Diagram Element Breakdown

Fraud Bot (Emulator/Script)

This represents the origin of the fraud. Bots are software programs designed to automate repetitive tasks. In this context, they operate on emulators (software that mimics mobile hardware on a computer) or scripts running on real devices to simulate human gameplay without any actual human involvement. This is the engine that drives the fraudulent activity at scale.

Fake In-Game Actions

This stage represents the bot’s behavior within the game. Instead of genuine engagement, the bot performs simple, repeatable tasks like collecting rewards or finishing levels. This is crucial for the fraud to work, as many in-game ads are only shown after a user achieves a certain milestone. The bot’s actions are designed solely to reach these ad-triggering checkpoints.

Trigger Ad Event & Simulate Click/In-App Purchase

This is the monetization point for the fraudster. After performing the necessary actions, the bot triggers an ad to be displayed and then fakes an interaction, such as a click, view, or even a purchase. This action is what generates a payout. For example, the bot might simulate a click on a banner ad or “watch” a rewarded video to completion, each action being a billable event for the advertiser.

Traffic Security System

This element represents the defense mechanism. A traffic security system analyzes incoming data from user actions to spot anomalies. It looks for patterns indicative of bot behavior, such as impossibly fast level completion, repetitive actions across many “users,” or clicks originating from data centers instead of residential IPs. Its function is to identify and block the fraudulent actions before they are counted as legitimate and billed to the advertiser.

🧠 Core Detection Logic

Example 1: Behavioral Heuristics

This logic identifies non-human patterns by analyzing the timing and sequence of events within a user session. Real players exhibit variability in their actions, while bots are often repetitive and unnaturally fast. This check fits within a real-time traffic filtering layer to flag suspicious sessions for deeper inspection or immediate blocking.

FUNCTION check_behavioral_heuristics(session_events):
  // Rule 1: Check for unnaturally short time between key events
  level_start_time = session_events.get_event_time("level_start")
  level_complete_time = session_events.get_event_time("level_complete")
  time_to_complete = level_complete_time - level_start_time

  IF time_to_complete < MIN_LEGITIMATE_DURATION:
    RETURN "FLAGGED: Impossibly fast completion"

  // Rule 2: Check for repetitive, non-varying action sequences
  action_pattern = session_events.get_action_sequence()
  IF is_highly_repetitive(action_pattern):
    RETURN "FLAGGED: Repetitive bot-like behavior"

  // Rule 3: Check for ad interaction without core gameplay
  IF session_events.has("ad_click") AND NOT session_events.has("core_gameplay_action"):
    RETURN "FLAGGED: Ad interaction without engagement"

  RETURN "PASSED"

Example 2: Purchase Receipt Validation

This logic validates in-app purchases that trigger cost-per-action (CPA) ad events. It ensures a purchase is legitimate by checking its transaction receipt with the corresponding app store (Apple or Google). This is a critical backend process to prevent fraudsters from faking high-value conversion events.

FUNCTION validate_purchase_event(purchase_data):
  receipt = purchase_data.get_receipt()
  transaction_id = purchase_data.get_transaction_id()
  app_id = purchase_data.get_app_id()

  IF receipt IS NULL:
    RETURN "REJECTED: Missing purchase receipt" // Flag suspicious transactions without receipts.

  // Send receipt to the app store's validation service
  is_valid_receipt = app_store_api.validate(receipt, transaction_id)

  IF NOT is_valid_receipt:
    RETURN "REJECTED: Invalid app store receipt"

  // Check if receipt details match the transaction
  receipt_app_id = receipt.get_app_id()
  IF app_id != receipt_app_id:
    RETURN "REJECTED: App ID mismatch"

  RETURN "VERIFIED"

Example 3: Device Integrity & Environment Check

This logic checks the user's device for signs of tampering or virtualization, which are common hallmarks of bot farms. It identifies emulators, rooted (Android) or jailbroken (iOS) devices, and other environments frequently used to scale fraudulent activities. This check is often performed by an SDK integrated into the app.

FUNCTION check_device_integrity(device_info):
  // Check 1: Detect if the app is running in an emulator
  IF device_info.is_emulator():
    RETURN "FLAGGED: Running on an emulator"

  // Check 2: Detect if the device has root or jailbreak access
  IF device_info.is_rooted() OR device_info.is_jailbroken():
    RETURN "FLAGGED: Compromised device (root/jailbreak)"

  // Check 3: Check for known fraudulent device signatures
  device_signature = device_info.get_fingerprint()
  IF is_in_fraud_database(device_signature):
    RETURN "FLAGGED: Device signature is blacklisted"

  // Check 4: Check for VPN or Proxy usage to mask location
  IF device_info.is_using_proxy() OR device_info.is_using_vpn():
    RETURN "FLAGGED: Connection masked by Proxy/VPN"

  RETURN "PASSED"

📈 Practical Use Cases for Businesses

• Campaign Shielding – Protects cost-per-install (CPI) and cost-per-action (CPA) campaigns by ensuring payments are for real users and events, not bot-generated fakes. This directly prevents budget waste on fraudulent channels.
• Data Integrity for LTV – Ensures that user lifetime value (LTV) models are built on clean data from genuine players, leading to more accurate revenue forecasting and smarter user acquisition strategies.
• Blocking Ad Farming – Prevents bots from repeatedly "watching" rewarded video ads or clicking banners solely to generate ad revenue for a fraudulent publisher, which protects brand safety and marketing ROI.
• Preventing Chargeback Fraud – In cases where fraudsters use stolen credit cards for in-app purchases, detection helps refuse the transaction, saving the business from chargeback fees and revenue loss.

Example 1: Session Velocity Rule

This logic flags users who trigger an unusually high number of ad events in a short period, a strong indicator of automated ad farming. It's used to protect the budget allocated for rewarded ads.

// Logic to detect ad farming velocity
DEFINE RULE high_ad_velocity:
  WHEN user_session
  AGGREGATE COUNT(rewarded_ad_view) AS ad_count
  OVER 1_HOUR

  CONDITION:
    ad_count > 25

  ACTION:
    FLAG_USER_AS_SUSPICIOUS
    PAUSE_ADS_FOR_USER(24_HOURS)

Example 2: Conversion Behavior Scoring

This pseudocode scores a user based on post-install behavior. A low score indicates a likely fake install, where the "user" installs the app to trigger a CPI event but shows no genuine engagement afterward.

// Score user quality post-install to validate install events
FUNCTION score_user_engagement(user_id, time_since_install):
  IF time_since_install > 72_HOURS:
    events = GET_USER_EVENTS(user_id)

    score = 0
    IF events.contains("tutorial_completed"): score += 20
    IF events.contains("level_5_reached"): score += 30
    IF events.contains("made_in_app_purchase"): score += 50

    IF score < 10:
      // This user is likely a fake install with no real engagement
      MARK_INSTALL_AS_INVALID(user_id)
    ELSE:
      MARK_INSTALL_AS_VALID(user_id)

🐍 Python Code Examples

This code simulates checking for click flooding, where a single IP address generates an unreasonable number of clicks in a short time. This helps block IPs that are part of a botnet or click farm.

# Dictionary to store click timestamps for each IP
ip_click_logs = {}
CLICK_LIMIT = 10
TIME_WINDOW_SECONDS = 60

def is_click_flood(ip_address, current_time):
    """Checks if an IP is generating too many clicks."""
    if ip_address not in ip_click_logs:
        ip_click_logs[ip_address] = []

    # Remove clicks outside the time window
    ip_click_logs[ip_address] = [t for t in ip_click_logs[ip_address] if current_time - t < TIME_WINDOW_SECONDS]

    # Add the new click
    ip_click_logs[ip_address].append(current_time)

    # Check if the click limit is exceeded
    if len(ip_click_logs[ip_address]) > CLICK_LIMIT:
        print(f"FLAGGED: Click flood detected from IP {ip_address}")
        return True
    return False

# Example usage
import time
is_click_flood("192.168.1.101", time.time())

This example demonstrates how to identify bot traffic by analyzing user-agent strings. Bots often use generic, outdated, or inconsistent user agents that differ from those of legitimate mobile devices.

def analyze_user_agent(user_agent):
    """Analyzes a user-agent string for signs of being a bot."""
    suspicious_keywords = ["bot", "spider", "headless", "okhttp"]
    legit_mobile_keywords = ["Mobile", "Android", "iPhone"]

    ua_lower = user_agent.lower()

    # Rule 1: Check for explicit bot keywords
    if any(keyword in ua_lower for keyword in suspicious_keywords):
        print(f"FLAGGED: Suspicious keyword found in User-Agent: {user_agent}")
        return False

    # Rule 2: Check for presence of typical mobile identifiers
    if not any(keyword in user_agent for keyword in legit_mobile_keywords):
        print(f"FLAGGED: Non-standard or missing mobile identifier: {user_agent}")
        return False

    print("PASSED: User-Agent appears legitimate.")
    return True

# Example Usage
analyze_user_agent("Mozilla/5.0 (Linux; Android 12; Pixel 6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/104.0.0.0 Mobile Safari/537.36")
analyze_user_agent("python-requests/2.28.1")

Types of In game purchase

• Click Injection Fraud – A malicious app on a user's device detects when another app is being installed and triggers a fake click just before the installation completes. This allows the fraudster to illegitimately claim credit for an organic install and receive a payout.
• Ad Farming Bots – Automated scripts or bots run inside a game with the sole purpose of repeatedly viewing ads to collect in-game rewards. This generates a high volume of fake ad impressions, draining advertiser budgets on users who have no genuine interest in the game or the ads.
• Purchase Receipt Forgery – Fraudsters use hacked or fake purchase receipts to trick an app into unlocking premium content or features. When this fake purchase is tied to a Cost-Per-Action (CPA) campaign, it causes the advertiser to pay for a nonexistent sale.
• SDK Spoofing – Bots mimic the communication signals sent by a legitimate ad SDK (Software Development Kit) to an ad network's servers. This creates completely fabricated ad impressions and clicks without an ad ever being displayed in a real app, making it appear as if legitimate activity occurred.
• Refund Abuse for CPA – A user, often a fraudster, makes a legitimate in-app purchase using a real payment method to trigger a CPA event for an advertiser. Shortly after, they request a refund from the app store, effectively getting the product for free while the advertiser still pays for the "conversion."

🛡️ Common Detection Techniques

• Behavioral Biometrics – This technique analyzes how a user physically interacts with their device, such as the patterns, speed, and pressure of their swipes and taps. It can distinguish between nuanced human behavior and the uniform, repetitive actions of a bot.
• Click-to-Install Time (CTIT) Analysis – By measuring the time between an ad click and the subsequent app installation, this method detects anomalies. Impossibly short times can indicate click injection fraud, while extremely long times might signal click flooding from a user who wasn't genuinely influenced by the ad.
• Device Fingerprinting – This involves collecting a unique set of identifiers from a device, like its OS version, hardware model, and IP address. It helps detect fraud by identifying when thousands of "installs" originate from a small number of actual devices, a hallmark of bot farms.
• Purchase Validation – This technique cross-references an in-app purchase receipt with the official app store (Google Play or Apple App Store) to confirm its authenticity. It is highly effective at preventing fraud where users forge receipts to unlock paid content or trigger CPA events.
• Session Heuristics – This method involves analyzing a user's gameplay session for logical inconsistencies. For example, it flags sessions where a player levels up faster than humanly possible or interacts with ads without ever engaging in core gameplay mechanics.

🧰 Popular Tools & Services

Tool	Description	Pros	Cons
MobileGuard Pro	An SDK-based solution that focuses on device integrity and behavioral analysis to detect bots, emulators, and other signs of in-game automation in real-time.	Deep integration provides granular data; strong against sophisticated bots; real-time blocking capabilities.	Requires app-level integration which can be complex; may add slight performance overhead to the application.
TrafficValida.io	A cloud-based platform that analyzes traffic data post-click from multiple ad networks. It uses machine learning to identify large-scale fraud patterns like click flooding and attribution fraud.	Easy to integrate with ad networks; provides a centralized view across all campaigns; effective at spotting network-level fraud.	Primarily post-mortem analysis; less effective at blocking fraud in real-time before the click is paid for.
AdSecure Shield	Specializes in verifying ad impressions and clicks. It ensures ads are served to real users in the correct geo-locations and helps prevent SDK spoofing and ad stacking.	Strong focus on impression and click quality; good for ensuring brand safety; provides detailed placement reports.	May not catch complex in-app behavioral fraud or purchase-related schemes; focused more on the ad call than post-install events.
ClickIntegrity Platform	A comprehensive suite that combines IP blacklisting, device fingerprinting, and behavioral checks. It offers customizable rules to target specific fraud types affecting mobile campaigns.	Highly customizable; combines multiple detection methods for layered security; provides actionable reports.	Requires active management and rule-tuning to remain effective; can be expensive for smaller developers.

📊 KPI & Metrics

To effectively manage fraud related to in-game purchases and ad events, it's crucial to track metrics that measure both the accuracy of fraud detection and its impact on business goals. Monitoring these KPIs helps quantify the effectiveness of prevention tools and ensures that advertising budgets are protected while maximizing return on investment.

Metric Name	Description	Business Relevance
Invalid Traffic (IVT) Rate	The percentage of ad clicks or impressions identified as fraudulent or non-human.	A primary indicator of overall fraud levels and the effectiveness of filtering solutions.
Invalid Install Rate	The percentage of app installs attributed to fraudulent sources like bots or click farms.	Directly measures waste in user acquisition campaigns and impacts Cost Per Install (CPI) calculations.
Cost Per Valid Install (CPVI)	The advertising cost divided by the number of verified, non-fraudulent installs.	Provides a true measure of user acquisition efficiency by excluding fraudulent installs.
False Positive Rate	The percentage of legitimate users or actions incorrectly flagged as fraudulent.	Crucial for ensuring fraud prevention does not harm user experience or block real customers.
Purchase Refund Rate	The percentage of in-app purchases that are later refunded by the user.	A high rate can indicate refund abuse, where users trigger a paid conversion and then reverse the charge.

These metrics are typically monitored through real-time dashboards provided by fraud detection services or mobile measurement partners. Sudden spikes in metrics like IVT rate or refund rate can trigger alerts for fraud analysts, who then investigate the source. The feedback from these investigations is used to refine detection rules, update blacklists, and optimize the overall security posture to adapt to new fraud tactics.

🆚 Comparison with Other Detection Methods

Behavioral Analysis vs. IP Blacklisting

Analyzing in-game behavior is more precise than generic IP blacklisting. Blacklisting blocks entire IP ranges known for fraud, which is fast but can incorrectly block legitimate users sharing an IP (e.g., on a mobile carrier network). Behavioral analysis focuses on actions, flagging a specific user for suspicious activity (like impossibly fast gameplay) without affecting other users on the same IP. It is more accurate but requires more processing power.

In-App Heuristics vs. CAPTCHA

Using in-game heuristics provides a frictionless user experience compared to CAPTCHA challenges. CAPTCHAs are effective at stopping simple bots but are highly disruptive in a gaming environment and ruin immersion. In-app behavioral checks work silently in the background, analyzing gameplay patterns to identify bots without ever interrupting the real player. They are better suited for maintaining player engagement but can be more complex to implement correctly.

Purchase Receipt Validation vs. Signature-Based Filtering

Validating a purchase receipt directly with the app store is a definitive way to confirm a transaction, unlike signature-based filtering which looks for known patterns of fraudulent traffic. Signature-based methods are effective against common, known bot types but are useless against new or sophisticated fraud techniques. Receipt validation is nearly foolproof for confirming a single transaction's legitimacy but is a reactive method used for verification after the event occurs.

⚠️ Limitations & Drawbacks

While analyzing in-game behavior is a powerful method for fraud detection, it has limitations. It can be resource-intensive, and its effectiveness depends heavily on the sophistication of the fraudster. Sometimes, the line between a highly skilled player and a bot can be difficult to distinguish, leading to potential errors.

• High Implementation Complexity – Integrating deep behavioral tracking requires significant development effort within the game's code, unlike simpler, server-side methods like IP blocking.
• Performance Overhead – Constant monitoring and analysis of in-game events can consume device resources like CPU and battery, potentially impacting the gameplay experience for legitimate users.
• Evolving Bot Sophistication – Fraudsters continually improve their bots to mimic human behavior more accurately, using dynamic scripts and AI to evade detection based on static rules.
• False Positives – Overly strict rules may incorrectly flag highly skilled or unconventional players as bots, leading to a negative user experience and potential loss of legitimate customers.
• Delayed Detection for Post-Install Fraud – Some fraudulent activities, like refund abuse, can only be confirmed days or weeks after the initial install event, meaning the advertiser may have already paid for the fraudulent conversion.
• Limited Scope – This method is highly effective for in-game events but offers little protection against fraud types that occur outside the app, such as click injection or attribution fraud happening at the ad network level.

In scenarios with high traffic volume or rapidly changing fraud tactics, a hybrid approach combining behavioral analysis with machine learning and network-level filtering is often more suitable.

❓ Frequently Asked Questions

How does in-game ad fraud affect my campaign's return on ad spend (ROAS)?

It directly harms ROAS by wasting your budget on fake users who will never make legitimate purchases. Bots generate fraudulent installs or trigger ad events, making your campaign metrics look good while delivering zero actual value, thus lowering your true return.

Can't I just block traffic from data centers to stop this fraud?

While blocking data center IPs is a good first step, it is not a complete solution. Fraudsters are increasingly using residential proxies and vast networks of real, compromised mobile devices (bot farms) to make their traffic appear legitimate and bypass simple IP-based rules.

Is this type of fraud more common in certain game genres?

Yes, it's particularly prevalent in hyper-casual and reward-based games where ad monetization is central to the business model. Games that offer in-game currency or items for watching ads create a direct incentive for fraudsters to automate that process with bots.

How is this different from standard click fraud?

Standard click fraud often involves bots simply clicking an ad link. Fraud related to in-game events is more sophisticated. It requires the bot to perform a series of actions post-install—like completing a level or simulating a purchase—to trigger a conversion event that the advertiser pays for.

Does implementing in-game fraud detection slow down my app?

It can if not implemented efficiently. Most modern fraud detection SDKs are optimized to have minimal impact on performance. However, intensive real-time analysis of user behavior can consume some device resources, so a balance must be struck between security and user experience.

🧾 Summary

In the context of ad fraud prevention, "in-game purchase" refers to the exploitation of monetized events within a mobile game. Fraudsters use bots to simulate gameplay and trigger fake ad views, installs, or purchases, which drains advertising budgets on non-genuine user actions. Detecting this activity by analyzing behavioral patterns and validating transactions is crucial for protecting ad spend and ensuring data integrity.

🔗 Related Articles

• Fighting Fraud in Mobile Gaming Advertising: Protecting ROI & Reputation | Z2A Digital – https://z2a.digital/blog/fighting-fraud-in-mobile-gaming-advertising-protecting-roi-reputation/
• Preventing Mobile Click-Fraud Before It Starts - Appdome – https://www.appdome.com/dev-tip/prevent-mobile-click-fraud-in-any-app-without-coding/
• Mobile Fraud Detection: Types, Techniques & Best Practices - FOCAL – https://www.focallabs.com/blog/mobile-fraud-detection
• The Mobile Gaming Industry & Click Fraud | TrafficGuard – https://www.trafficguard.ai/blog/the-mobile-gaming-industry-and-click-fraud
• Bots are invading your mobile games: Take them on! - Irdeto Insights – https://irdeto.com/insight/bots-are-invading-your-mobile-games-take-them-on/