In app notifications

How In app notifications Works

  +-----------------+      +-----------------+      +---------------------+      +----------------+
  |   User Action   | →    | Suspicion Trigger| →  | Notification Challenge| →  |  User Response |
  | (e.g., Ad Click)|      | (e.g., High Freq) |    |  (Active or Passive)  |    | (Tap or No-Tap)|
  +-----------------+      +-----------------+      +---------------------+      +----------------+
                                                                                       │
                                                                                       │
                                                                                       ▼
                                                                                +-------------+
                                                                                |  Validation |
                                                                                +-------------+
                                                                                       │
                                                                                       └─→ Invalid Traffic (Block)
                                                                                       │
                                                                                       └─→ Valid Traffic (Allow)

In-app notifications serve as a dynamic security mechanism within a mobile application to differentiate between legitimate human users and fraudulent automated bots. Rather than relying solely on server-side data analysis after a click has occurred, this method actively engages with the user or their environment in real-time. The core idea is to introduce a verification step that is trivial for a human to pass but difficult for a simple bot to anticipate or replicate. This client-side verification adds a crucial layer of defense, making ad fraud more difficult to execute at scale.

Initial Trigger and Analysis

The process begins when a user performs a monitored action, such as clicking on an advertisement. A traffic security system analyzes this initial event against a set of risk rules. These rules might flag activities like an abnormally high click frequency from a single device, clicks originating from a suspicious IP address or data center, or interactions that follow a predictable, non-human pattern. If an action is deemed suspicious, it triggers the in-app notification challenge instead of immediately registering the click as valid.

The Notification Challenge

Once triggered, the system deploys an in-app notification. This can be an active challenge, such as a simple banner asking the user to “Tap to confirm,” which requires direct interaction. Alternatively, it can be a passive or silent notification, invisible to the user. A silent notification can execute a small script in the background to check for signs of automation, such as verifying the device integrity, checking for emulator properties, or analyzing touch event data for human-like randomness. The type of challenge is often determined by the assessed risk level.

Validation and Scoring

The system then validates the outcome of the challenge. For an active challenge, it checks if the user responded correctly and measures the interaction latency—bots may fail to respond or respond with inhuman speed. For a passive challenge, it analyzes the data returned from the client-side script. Based on this validation, the user session is scored. A failed or ignored challenge results in a high-risk score, leading to the click being flagged as fraudulent and the source potentially blocked. A successful interaction confirms human presence, validates the traffic, and allows the action to proceed.

Diagram Breakdown

User Action and Trigger

The process starts with a `User Action`, typically a click on an ad. This action is fed into a `Suspicion Trigger` module, which is a rule-based engine that flags anomalies like rapid clicks, indicating non-human behavior.

Notification and Response

A flagged event initiates a `Notification Challenge`. This can be an active prompt or a passive, background check. The user’s `Response` (or lack thereof) is captured. A human will typically respond appropriately, while a bot will either ignore it or fail the check.

Validation and Action

The `Validation` stage analyzes the response. If the interaction is confirmed as legitimate, the traffic is marked as `Valid` and allowed. If the validation fails, the traffic is identified as `Invalid`, and the system can block the click and associated user or IP address.

🧠 Core Detection Logic

Example 1: Interactive Engagement Verification

This logic actively challenges a suspicious user to prove they are human. When a session is flagged for unusual behavior (e.g., too many clicks in a short time), a non-intrusive notification is displayed, requiring a simple tap to proceed. This separates automated scripts from real users.

FUNCTION onAdClick(user_session):
  // Check if click frequency is abnormally high
  IF user_session.click_count > 5 AND user_session.time_since_last_click < 1000ms THEN
    // Trigger an interactive notification challenge
    challenge_result = displayVerificationNotification("Please tap here to continue")

    IF challenge_result IS "success" THEN
      // User is likely human, validate the click
      RETURN "VALID_CLICK"
    ELSE
      // No interaction or failed challenge, flag as bot
      RETURN "FRAUDULENT_CLICK"
    END IF
  ELSE
    RETURN "VALID_CLICK"
  END IF
END FUNCTION

Example 2: Silent Environment Check

This logic uses a notification that is invisible to the user. It acts as a trigger to run a client-side script that collects device and environment data. This is useful for detecting emulators, rooted devices, or other signs that the environment is not a standard mobile device.

FUNCTION onAppLaunch(device_info):
  // Trigger a silent notification to run a background check
  triggerSilentCheck()

  // The check gathers environment data
  environment_data = getEnvironmentData()

  IF environment_data.is_emulator == TRUE OR environment_data.is_rooted == TRUE THEN
    // High-risk environment, flag all actions from this device
    SET device_info.trust_score = 0.1
    LOG "Device flagged as high-risk emulator."
  ELSE
    SET device_info.trust_score = 0.9
  END IF
END FUNCTION

Example 3: Latency-Based Anomaly Detection

This logic measures the time it takes for a user to interact with a notification. Human interaction has a natural, variable delay, whereas bots often react instantly or not at all. An extreme latency measurement can indicate automated activity.

FUNCTION onChallengeIssued(timestamp_issued):
  // Display a simple interactive notification
  displaySimpleNotification("Confirmation needed")

  // Wait for user interaction
  WAIT for interaction_event

  // Record the time of interaction
  timestamp_responded = getCurrentTime()
  latency = timestamp_responded - timestamp_issued

  // Check if latency is within human-like range (e.g., >200ms and <5000ms)
  IF latency < 200ms OR latency > 5000ms THEN
    // Inhuman response time, likely a bot
    FLAG "High-risk: Latency anomaly"
    RETURN FALSE
  ELSE
    // Human-like response time
    RETURN TRUE
  END IF
END FUNCTION

📈 Practical Use Cases for Businesses

• Campaign Budget Shielding – In-app notifications can issue challenges before an ad click is registered, preventing bots from consuming PPC budgets on fake interactions and ensuring that ad spend is allocated toward genuine potential customers.
• Data Integrity for Analytics – By weeding out non-human traffic, this method ensures that user engagement metrics (like session duration, conversion rates) are accurate. This gives businesses a true understanding of campaign performance and user behavior.
• Improving ROAS – By blocking fraudulent clicks and fake installs attributed to them, businesses can significantly improve their Return on Ad Spend (ROAS), as marketing funds are directed only at traffic with a real potential for conversion.
• User Quality Verification – For apps that rely on acquiring high-quality users, notifications can act as a screening tool to filter out low-quality traffic from bot farms or emulators before they contaminate the user base.

Example 1: Geofencing with a Notification Challenge

A business running a geo-targeted campaign can use notifications to verify users from outside the target area who are using VPNs or proxies. If a user's IP address doesn't match their device's GPS location, a challenge is issued.

FUNCTION onUserImpression(user_data):
  // Campaign targets USA only
  target_country = "US"
  ip_country = getCountryFromIP(user_data.ip)
  gps_country = getCountryFromGPS(user_data.gps)

  // Mismatch detected
  IF ip_country != gps_country AND ip_country != target_country THEN
    // Issue a challenge to verify the user
    is_human = issueInAppChallenge("Please confirm your location to proceed")
    IF is_human == FALSE THEN
      BLOCK_TRAFFIC(user_data.ip)
    END IF
  END IF
END FUNCTION

Example 2: Session Scoring with Passive Checks

An e-commerce app can use silent in-app notifications to score session quality. A notification can trigger checks for bot-like behavior such as unnaturally fast navigation through product pages or adding items to a cart in milliseconds.

FUNCTION analyzeSession(session_events):
  // Passively trigger checks during the session
  session_score = 100
  time_between_events = calculateTimeDiff(session_events)

  // Check for inhuman speed
  IF time_between_events < 50ms THEN
    session_score = session_score - 50
  END IF

  // Check for standard device properties
  device_integrity = runSilentDeviceCheck()
  IF device_integrity IS "Compromised" THEN
    session_score = session_score - 40
  END IF

  // If score is too low, invalidate clicks from this session
  IF session_score < 50 THEN
    INVALIDATE_SESSION_CLICKS(session_events.session_id)
  END IF
END FUNCTION

🐍 Python Code Examples

This Python code simulates checking for rapid-fire clicks from a user. If more than a certain number of clicks occur within a short window, a function to trigger a notification challenge is called.

import time

user_clicks = {}
CLICK_LIMIT = 5
TIME_WINDOW = 2  # in seconds

def on_click(user_id):
    current_time = time.time()
    if user_id not in user_clicks:
        user_clicks[user_id] = []
    
    # Add current click time and filter out old ones
    user_clicks[user_id].append(current_time)
    user_clicks[user_id] = [t for t in user_clicks[user_id] if current_time - t < TIME_WINDOW]
    
    # Check for suspicious activity
    if len(user_clicks[user_id]) > CLICK_LIMIT:
        print(f"Suspicious activity from {user_id}. Triggering notification challenge.")
        trigger_notification_challenge(user_id)
    else:
        print(f"Click from {user_id} is valid.")

def trigger_notification_challenge(user_id):
    # This would call the app's notification system
    print(f"Verification notification sent to {user_id}.")

# Simulate clicks
on_click("user-123")
time.sleep(0.1)
on_click("user-123")
time.sleep(0.1)
on_click("user-123")
time.sleep(0.1)
on_click("user-123")
time.sleep(0.1)
on_click("user-123")
time.sleep(0.1)
on_click("user-123")

This example demonstrates a function that scores traffic based on a mix of factors, including the result of a hypothetical in-app notification challenge. A low score would indicate fraudulent traffic.

def score_traffic(request_data):
    score = 100
    
    # Check if a notification challenge was passed
    if request_data.get("notification_challenge_passed") == False:
        score -= 50
        
    # Penalize traffic from known data centers
    if is_from_datacenter(request_data.get("ip_address")):
        score -= 30
        
    # Check for a suspicious user agent
    if "headless" in request_data.get("user_agent", "").lower():
        score -= 20
        
    print(f"IP {request_data.get('ip_address')} scored {score}")
    
    if score < 50:
        print("Traffic is likely fraudulent.")
        return "FRAUD"
    else:
        print("Traffic appears legitimate.")
        return "LEGITIMATE"

def is_from_datacenter(ip):
    # Dummy function: in reality, this would check against a database of data center IPs
    return ip.startswith("10.0.")

# Simulate scoring two different requests
legit_request = {"ip_address": "8.8.8.8", "user_agent": "Mozilla/5.0", "notification_challenge_passed": True}
fraud_request = {"ip_address": "10.0.0.1", "user_agent": "HeadlessChrome", "notification_challenge_passed": False}

score_traffic(legit_request)
score_traffic(fraud_request)

Types of In app notifications

• Active Challenge Notifications

  These are explicit prompts that require direct user interaction. For example, a banner or modal may appear asking the user to tap a button or solve a simple puzzle. Their purpose is to confirm active human presence and engagement.

• Passive Verification Notifications

  These notifications are invisible to the user and run in the background. They trigger a script to collect data about the device environment, such as checking for emulators, root access, or other signs of a non-standard, automated setup. This provides verification without interrupting the user experience.

• Contextual Triggered Alerts

  These notifications are deployed only when a user's behavior crosses a predefined risk threshold, such as an impossible number of clicks per second or navigating the app with inhuman speed. The alert serves as a real-time check in response to a specific suspicious action.

• Administrative Fraud Alerts

  Unlike user-facing notifications, these are alerts sent directly to system administrators or advertisers. They are triggered when a significant pattern of fraudulent activity is detected, providing real-time insights for manual review or automated blocking of the identified fraud source.

🛡️ Common Detection Techniques

• Interaction Latency Analysis

  This technique measures the time between when a notification challenge is presented and when the user interacts with it. Bots often respond with inhuman speed or not at all, making response time a strong indicator of human vs. automated behavior.

• Behavioral Triggering

  This involves issuing a notification challenge only after observing bot-like behavior. For example, if a user clicks an ad and navigates a landing page faster than a human possibly could, the system triggers a verification step to confirm authenticity.

• Client-Side Fingerprinting

  A silent notification is used to execute a script that collects detailed information about the client environment (e.g., OS, browser, screen resolution, available fonts). This "fingerprint" can be compared against known bot signatures or used to detect inconsistencies that suggest an emulated device.

• Honeypot Challenge

  This technique involves placing invisible interactive elements within the app that a human user would never see or click. Automated scripts that crawl the app's UI and click all available buttons will interact with the honeypot, triggering a notification that flags the session as a bot.

• App Integrity Verification

  A passive notification can trigger an API call to services like Google's SafetyNet or Apple's DeviceCheck. These services verify that the app has not been tampered with and is running on a genuine, certified device, which helps to block traffic from modified apps or emulators.

🧰 Popular Tools & Services

Tool	Description	Pros	Cons
TrafficGuard AI	A comprehensive fraud prevention suite that uses machine learning and customizable rules to detect and block invalid traffic across mobile and web campaigns. It often includes real-time alerts for suspicious activity.	Multi-channel protection; detailed reporting; real-time detection.	Can be expensive for smaller businesses; may require technical expertise for advanced configuration.
BotBlocker Suite	Focuses on bot detection by analyzing user behavior, device fingerprints, and network signals. It often uses challenges like CAPTCHAs or custom in-app notifications to differentiate bots from humans.	Effective against automated threats; provides detailed bot activity logs; easy to integrate.	May introduce friction for legitimate users; sophisticated bots can sometimes bypass challenges.
ClickVerify Platform	Specializes in click fraud detection for PPC campaigns. It monitors clicks for anomalies, such as high frequency from a single IP, and can automatically add fraudulent sources to an exclusion list.	Protects ad spend effectively; simple to use for marketers; provides clear reports on blocked clicks.	Primarily focused on click fraud, may not cover other fraud types like impression or install fraud.
AdSecure Shield	An ad verification tool that focuses on the creative and landing page quality. It can identify and block malicious ads (malvertising) and non-compliant creatives, indirectly reducing fraudulent interactions.	Protects brand safety; prevents malicious redirects; ensures compliance with ad network policies.	Less focused on sophisticated click fraud or bot detection; more of a creative compliance tool.

📊 KPI & Metrics

Tracking the performance of in-app notification systems for fraud prevention is crucial for measuring both their effectiveness and their impact on the user experience. It's important to monitor not only how accurately fraud is detected but also the business outcomes, such as budget savings and improvements in data quality.

Metric Name	Description	Business Relevance
Fraud Detection Rate (FDR)	The percentage of total fraudulent clicks or actions that were correctly identified by the system.	Measures the core effectiveness of the fraud prevention system in catching threats.
False Positive Rate (FPR)	The percentage of legitimate user actions that were incorrectly flagged as fraudulent.	Indicates the impact on user experience; a high rate can block real users and harm revenue.
Challenge Completion Rate	The percentage of users who successfully complete an active in-app notification challenge.	Helps optimize the difficulty and intrusiveness of challenges to minimize user friction.
Blocked Clicks/Impressions	The absolute number of clicks or other actions blocked by the system.	Translates directly into ad budget saved from being spent on invalid traffic.
Clean Traffic Ratio	The proportion of traffic deemed legitimate after filtering out fraudulent activities.	Provides a high-level view of traffic quality and the overall health of advertising channels.

These metrics are typically monitored through real-time dashboards and analytics platforms. Feedback loops are established where insights from these KPIs, especially a rising False Positive Rate, are used to tune the sensitivity of fraud filters and rules. Automated alerts can also be set up to notify teams of sudden spikes in fraudulent activity or unusual metric behavior.

🆚 Comparison with Other Detection Methods

Accuracy and Nuance

Compared to static IP blacklisting, in-app notifications offer more nuanced and accurate detection. IP blacklisting can be a blunt instrument, blocking entire ranges of IPs and inadvertently affecting legitimate users. In-app notifications, however, challenge individual suspicious sessions based on real-time behavior, reducing the risk of false positives and providing a more precise way to verify users.

Real-Time vs. Post-Click Analysis

In-app notifications are a real-time detection method, acting before a fraudulent click is fully registered and paid for. This contrasts with many forms of behavioral analytics that perform post-click or batch analysis. While post-click analysis is powerful for identifying large-scale patterns, the real-time nature of notifications allows for immediate prevention, saving ad spend instantly.

User Friction and Implementation

Compared to CAPTCHAs, which are often highly disruptive to the user experience, in-app notifications can be less intrusive. A simple tap-to-verify banner or a completely silent background check introduces less friction. However, implementing a custom in-app notification system is generally more complex than adding a standard CAPTCHA service, as it requires deeper integration with the app's logic and UI.

Effectiveness Against Bots

In-app notifications are particularly effective against simple to moderately sophisticated bots that are not programmed to handle unexpected, dynamic UI elements. More advanced behavioral analytics may be superior at detecting sophisticated bots that can mimic human behavior more convincingly. The best approach often involves a layered strategy, using notifications to filter out basic bots and behavioral analysis for more advanced threats.

⚠️ Limitations & Drawbacks

While effective, using in-app notifications for fraud prevention has limitations. The method's success depends heavily on the context of the app and the sophistication of the fraud being attempted. In some scenarios, it can be inefficient or even detrimental to the user experience if not implemented carefully.

• User Friction – Active challenges, even simple ones, can interrupt the user's flow and may be perceived as an annoyance, potentially leading to lower engagement or app abandonment.
• Implementation Complexity – Developing and integrating a custom in-app notification challenge system requires significant development effort compared to using third-party, plug-and-play solutions.
• Limited to App Environments – This method is inherently designed for mobile app environments and is not applicable for protecting web-based or desktop advertising campaigns from click fraud.
• Evasion by Sophisticated Bots – Advanced bots can be programmed to recognize and correctly interact with common notification challenges, rendering the method less effective against high-end automated threats.
• Potential for False Positives – If the rules that trigger notifications are too strict, the system may challenge and inconvenience legitimate users, creating a negative experience for genuine customers.
• Maintenance Overhead – The rules and triggers for notifications must be constantly updated and maintained to keep up with evolving fraud tactics, requiring ongoing resources.

In cases of highly sophisticated bot attacks or where user friction is a primary concern, fallback or hybrid strategies combining passive analysis with server-side detection might be more suitable.

❓ Frequently Asked Questions

Can in-app notifications negatively affect the user experience?

Yes, if not implemented carefully. Active challenges can interrupt the user journey. To minimize impact, it is best to use passive (silent) notifications where possible or trigger active challenges only for the most high-risk sessions. A/B testing can help find the right balance between security and user experience.

Is this method effective against all types of click fraud?

It is highly effective against simple to moderately sophisticated bots that cannot handle dynamic UI challenges. However, it may be less effective against the most advanced bots designed to mimic human behavior or manual click farms. It is best used as part of a layered security approach.

How does this differ from a standard CAPTCHA?

While both are challenge-response systems, in-app notifications can be more seamlessly integrated into the app's native UI and can be made less intrusive (e.g., a simple banner tap). They also offer passive, invisible checks, which a standard CAPTCHA does not. CAPTCHAs are typically a more disruptive, one-size-fits-all solution.

Can in-app notifications be used to prevent fraud outside of ad clicks?

Yes. The same principles can be applied to protect against other fraudulent activities, such as fake account creation, spamming within the app, promo abuse, or fraudulent in-app purchases. The notification acts as a general-purpose human verification step for any critical user action.

What data is collected during a passive notification check?

A passive or silent check typically collects non-personal, environmental data. This can include device information (model, OS version), screen resolution, app integrity status (checking if the app has been tampered with), and sensor data to check for human-like device movement. It is designed to verify the environment's authenticity without compromising user privacy.

🧾 Summary

In-app notifications serve as a powerful, client-side tool in digital advertising fraud prevention. By issuing real-time challenges—either active prompts or silent background checks—this method effectively verifies human interaction within an application. Its primary role is to distinguish genuine users from automated bots before fraudulent clicks are registered, thereby protecting advertising budgets, ensuring data accuracy, and improving overall campaign integrity.

🔗 Related Articles

• Efficient and Deployable Click Fraud Detection for Mobile Applications – https://www.researchgate.net/publication/348595532_Efficient_and_Deployable_Click_Fraud_Detection_for_Mobile_Applications
• "Click Fraud Detection in Online and In-app Advertisements: A Learning " by Thejas Gubbi Sadashiva – https://digitalcommons.fiu.edu/etd/4320/
• What is Mobile Ad Fraud? Detection & Prevention Guide - Spider AF – https://spideraf.com/media/blog/ad-fraud/what-is-mobile-ad-fraud
• AI-Based Techniques for Ad Click Fraud Detection and Prevention: Review and Research Directions - MDPI – https://www.mdpi.com/2076-3417/12/23/12030
• Want to get ahead of mobile ad fraud in 2024? Leverage Pulse's smart alerts - Adjust – https://www.adjust.com/blog/pulse-smart-alerts-for-mobile-ad-fraud/