❓ What is a Push notifications : definition, examples of use.

What is Push notifications?

Push notifications, in the context of fraud prevention, are messages sent to a user’s device to verify their authenticity. This technique helps distinguish real users from bots by requiring an interaction or background validation that automated scripts typically cannot perform, thus preventing click fraud and protecting traffic quality.

How Push notifications Works

  User Action               Server-Side Analysis             Challenge & Response
+-----------------+      +-----------------------+        +------------------------+
|   Clicks Ad     |----->|  Receives Click Data  |------>|   Sends Silent Push    |
| (App/Website)   |      |   (IP, User Agent)    |        | (Validation Token)     |
+-----------------+      +-----------------------+        +------------------------+
                                      │                             │
                                      │                             ▼
                                      │                      +----------------+
                                      │                      | Device Receives|
                                      │                      | & Responds OK? |
                                      │                      +----------------+
                                      │                             │
                                      ▼                             ▼
                                +---------------------------------------------------+
                                |                  Verification Logic               |
                                |  (Is IP suspicious? Does Push response match?)    |
                                +---------------------------------------------------+
                                                  │
                                                  ▼
                                     +-------------------------+
                                     |   Fraudulent / Valid    |
                                     |         Decision        |
                                     +-------------------------+

In digital advertising, push notifications serve as a powerful secondary verification channel to differentiate legitimate human users from fraudulent bots. The system leverages the native push notification services of operating systems (like Google’s FCM and Apple’s APNs) to issue a challenge that is difficult for simple automated scripts to solve.

Initial Click & Data Capture

The process begins when a user clicks on an ad. The publisher’s or ad network’s server captures initial data points associated with the click event. This includes standard information like the user’s IP address, device type, operating system, and user agent string. This data provides the first layer of analysis for identifying immediate red flags, such as traffic from known data centers or suspicious user agents.

Server-Side Challenge

If the initial data doesn’t immediately confirm the click as fraudulent, the traffic security system can issue a challenge. Instead of a visible notification, it often sends a silent push notification. This is a background data packet sent to the device’s registered push notification token. It’s invisible to the user and is designed to simply check if a legitimate, active application on a real device receives the payload and responds.

Response & Verification

A legitimate device with the app installed will receive the silent push via its operating system’s push service and can be programmed to send a confirmation back to the server. The fraud detection system then validates this response. If a response is received, it strongly indicates that the click originated from a real device. If no response arrives, the system can flag the click as likely fraudulent, as the device token may be fake or belong to an emulated device in a bot farm. This entire process happens in near real-time.

Diagram Element Breakdown

User Action -> Server-Side Analysis

This flow represents the initial data transmission. When a user clicks an ad, their device sends a request to the server, which includes a packet of information essential for the first stage of fraud analysis.

Server-Side Analysis -> Challenge & Response

This represents the decision to escalate verification. Based on the initial data, the server initiates a push notification challenge to actively probe the user’s device for authenticity beyond passive data points.

Challenge & Response -> Verification Logic

This path shows the result of the challenge. The device’s response (or lack thereof) is a critical piece of data that is fed back into the main verification system for a final decision.

Verification Logic -> Final Decision

This is the concluding step where all collected data—initial click info, IP reputation, and the push challenge result—is aggregated to score the click and definitively label it as either valid or fraudulent.

🧠 Core Detection Logic

Example 1: Silent Push Handshake

This logic validates a user session by sending a background notification to the device’s registered push token. It confirms that the click originates from a genuine device with the app installed, rather than an emulated device or bot farm where push tokens are invalid or inactive. This is a core technique for weeding out non-human traffic.

FUNCTION handle_click(user_session):
  // 1. Get device push token from session data
  device_token = user_session.get_push_token()

  // 2. Check if token is potentially valid
  IF is_valid_format(device_token) == FALSE:
    RETURN "FRAUDULENT (Invalid Token Format)"

  // 3. Send silent (background) push notification
  push_id = send_silent_push(device_token, "validation_challenge")
  
  // 4. Wait for a short period for device to respond
  WAIT for 5 seconds
  
  // 5. Check if a response for this push_id was received
  IF has_received_response(push_id):
    RETURN "VALID"
  ELSE:
    RETURN "FRAUDULENT (No Push Response)"
  ENDIF

Example 2: Push Interaction Latency

This logic analyzes the time between when a visible push notification is sent and when the user interacts with it. Bots often react instantaneously (or not at all), while human interaction has a more natural delay. Abnormally fast or non-existent interactions are flagged as suspicious. This helps detect automated scripts designed to mimic engagement.

FUNCTION analyze_push_interaction(push_event):
  time_sent = push_event.timestamp_sent
  time_clicked = push_event.timestamp_clicked

  // Calculate time-to-click in seconds
  latency = time_clicked - time_sent

  // Rule: Flag clicks that are too fast or take too long
  IF latency < 1.0: // Less than 1 second is non-human
    RETURN "FLAGGED (Likely Bot)"
  ELSE IF latency > 3600: // More than 1 hour is low engagement
    RETURN "IGNORE (Stale Click)"
  ELSE:
    RETURN "VALID (Human-like Latency)"
  ENDIF

Example 3: Geo-Mismatch Validation

This logic compares the IP address location of the initial ad click with the device’s locale or timezone information, which can be requested via a push notification that triggers a background check in the app. A significant mismatch (e.g., a click from a Virginia IP on a device set to a Russian timezone) is a strong indicator of proxy usage or sophisticated bot activity.

FUNCTION validate_geo_consistency(click_data, push_response):
  ip_location = get_location_from_ip(click_data.ip_address)
  device_timezone = push_response.get_device_timezone()

  // Check if IP country matches timezone region
  IF ip_location.country != get_country_from_timezone(device_timezone):
    // Mismatch detected
    increment_fraud_score(click_data.session_id, 50)
    RETURN "SUSPICIOUS (Geo Mismatch)"
  ELSE:
    RETURN "VALID (Geo Consistent)"
  ENDIF

📈 Practical Use Cases for Businesses

Campaign Budget Shielding – Use silent push notifications to pre-validate clicks, ensuring ad spend is allocated only to traffic from authentic devices, thereby preventing budget drainage from bot farms.
Lead Quality Assurance – For lead generation campaigns, a successful push interaction serves as a powerful signal of user intent and authenticity, helping businesses filter out fake form submissions and focus on high-quality leads.
Audience Integrity – By verifying devices through push challenges, businesses can ensure their retargeting audiences and analytics data are not polluted by bot traffic, leading to more accurate performance metrics and better decision-making.
Conversion Fraud Prevention – In e-commerce, push notifications can be used to validate high-value actions like account creation or coupon claims, preventing bots from abusing promotional offers.

Example 1: Pre-Bid Traffic Validation

In a programmatic environment, a publisher can use silent push data to score traffic quality. Before offering an ad impression at auction, the system checks if the user’s device has recently and successfully responded to a silent push, flagging it as “Premium” if it has.

FUNCTION get_traffic_quality(user_id):
  last_push_response = query_database(user_id, "last_push_response_time")
  
  IF last_push_response exists AND (current_time() - last_push_response < 24 hours):
    RETURN "PREMIUM_TRAFFIC"
  ELSE:
    RETURN "STANDARD_TRAFFIC"
  ENDIF

Example 2: Gated Content Access

A business offering a free e-book or report can protect against bots scraping the content by requiring a push notification confirmation before granting the download. This ensures a real user on a specific device is accessing the material.

FUNCTION grant_download_access(user_id):
  // User clicks "Download"
  device = get_user_device(user_id)
  
  // Send a visible push notification with "Confirm Download" button
  send_actionable_push(device.token, "Confirm your download request.")
  
  // Server waits for confirmation click
  IF wait_for_push_confirmation(user_id, timeout=60):
    // User confirmed
    RETURN "ACCESS_GRANTED"
  ELSE:
    // No confirmation
    RETURN "ACCESS_DENIED"
  ENDIF

🐍 Python Code Examples

This code demonstrates a basic filter for incoming ad clicks. It checks if a device token associated with the click exists in a set of known fraudulent tokens, a common first step in blocking invalid traffic before processing it further.

# A blocklist of known fraudulent device tokens
FRAUDULENT_TOKENS = {"token123_fake", "token456_bot", "token789_emulator"}

def filter_click_by_token(click_event):
    """
    Checks if a click's device token is on a blocklist.
    """
    device_token = click_event.get("device_token")
    if device_token in FRAUDULENT_TOKENS:
        print(f"Blocking fraudulent click from token: {device_token}")
        return False  # Block the click
    print(f"Allowing valid click from token: {device_token}")
    return True  # Allow the click

This example simulates scoring traffic based on the timing of push notification interactions. It penalizes clicks that happen too quickly after a notification is sent, as this behavior is characteristic of automated bots rather than human users.

import time

def score_push_interaction_speed(notification_sent_time, click_time):
    """
    Scores traffic based on the time between sending a push and getting a click.
    A lower score indicates higher fraud risk.
    """
    interaction_delay = click_time - notification_sent_time
    
    if interaction_delay < 2.0:  # Less than 2 seconds is suspicious
        return 10  # Low score, high risk
    elif interaction_delay < 10.0:
        return 70 # Medium score
    else:
        return 95 # High score, low risk

# Simulate an event
push_sent_at = time.time()
time.sleep(1.5) # Simulate a bot clicking very fast
user_clicked_at = time.time()

fraud_score = score_push_interaction_speed(push_sent_at, user_clicked_at)
print(f"Traffic authenticity score (0-100): {fraud_score}")

Types of Push notifications

User-Visible Notifications - Standard alerts that appear on a user's screen. In fraud detection, they act as an active challenge, requiring a user to tap a button to confirm an action. This method directly verifies user presence and engagement, as simple bots cannot typically perform this interaction.
Silent Push Notifications - These are background data packets sent to an app that do not trigger any alert for the user. Their purpose is to "ping" the device to verify it is genuine and online, or to trigger the app to send back device state information, effectively filtering out fake or emulated devices without user friction.
Rich Push Notifications - These are enhanced notifications that include images, GIFs, or interactive buttons (e.g., "Yes/No"). For fraud prevention, they can serve as a more complex challenge than a simple click, requiring a bot to parse more complex information or perform a specific action, thus increasing the difficulty of mimicry.
Actionable Notifications - A subset of rich notifications that provide direct action buttons within the alert. A common use case is a "Confirm Login" or "Verify Purchase" prompt. This requires explicit, contextual confirmation from a user, making it highly effective at preventing fraudulent automated actions.

🛡️ Common Detection Techniques

Device Token Validation - This involves checking the push notification device token (from services like APNs or FCM) to ensure it is correctly formatted and legitimate. Failed or malformed tokens are a primary indicator of an emulated device or a fraudulent client trying to spoof its identity.
Silent Push Probing - This technique sends frequent, invisible background notifications to registered devices. If the push service reports a failure, it indicates the app was uninstalled or the device is offline, helping to purge inactive or fraudulent users from audience lists.
Interaction Analysis - This method analyzes user behavior in response to a visible push notification, such as time-to-click, interaction patterns, and conversion rates. Abnormally fast or predictable interactions are strong signals of bot activity trying to mimic human engagement.
Behavioral Fingerprinting - After a successful push response confirms a real device, subsequent in-app actions are tracked to build a behavioral baseline. Deviations from this baseline, such as unusually high click activity, can be flagged as fraudulent even if the device itself is real.
Geo-Temporal Consistency Check - This technique correlates the timestamp and location of a push interaction with other user data points, like ad click time or server log location. Inconsistencies, such as an instant click from a different continent, reveal the use of proxies or other masking techniques.

🧰 Popular Tools & Services

Tool	Description	Pros	Cons
Silent Push	A threat intelligence platform that focuses on identifying malicious infrastructure before it is used in attacks. It helps protect against phishing, malvertising, and spoofing by providing preemptive intelligence.	Proactive threat detection, real-time alerts, seamless integration with existing security systems.	May require security expertise to fully leverage the intelligence data; focused more on infrastructure threats than on-page click fraud.
CleverTap	An engagement platform that uses push notifications to interact with users. In a security context, its real-time, trigger-based notifications can be used for fraud alerts like suspicious login attempts or transaction confirmations.	Highly customizable, supports rich and actionable notifications, provides detailed engagement analytics.	Primarily a marketing/engagement tool, so fraud prevention capabilities are a secondary use case and not its core function.
Opticks Security	An anti-fraud solution that analyzes traffic for suspicious activity using rules-based detection, fingerprinting, and machine learning. It is used by push ad networks to ensure traffic quality and prevent fraud.	Specialized in ad fraud, combines multiple detection methods, helps guarantee traffic integrity for campaigns.	Can be complex to integrate; primarily focused on the ad network side rather than individual advertiser implementation.
GeoEdge	An ad verification service that protects publishers from malicious ads, including those that initiate push notification scams or MFA fatigue attacks. It blocks bad ads in real-time before they are exposed to users.	Real-time ad blocking, protects the user experience, specializes in detecting cloaked ads and malicious redirects.	Focused on protecting publisher websites, not directly a tool for advertisers to manage their own click fraud.

📊 KPI & Metrics

Tracking metrics is crucial for evaluating the effectiveness of push notification-based fraud detection. It's important to monitor not only the technical accuracy of the detection methods but also their impact on business outcomes like campaign performance and return on investment.

Metric Name	Description	Business Relevance
Push Validation Rate	The percentage of clicks that successfully pass a push notification challenge.	Indicates the overall quality of a traffic source; low rates signal high levels of invalid or bot traffic.
Fraud Block Rate	The percentage of total clicks flagged as fraudulent by the push notification system.	Directly measures the volume of fraud being prevented, demonstrating the system's immediate value in protecting ad spend.
False Positive Rate	The percentage of legitimate clicks that are incorrectly flagged as fraudulent.	A critical accuracy metric; a high rate means you are blocking real customers and losing potential revenue.
Post-Validation Conversion Rate	The conversion rate of traffic that has been successfully verified by a push challenge.	Measures the true performance of ad campaigns on clean traffic, providing a more accurate ROI calculation.

These metrics are typically monitored through real-time dashboards that visualize traffic quality and fraud levels. Automated alerts are often configured to notify teams of sudden spikes in fraudulent activity or anomalies in validation rates. This feedback loop is essential for continuously tuning the detection rules and adapting to new threats without compromising the experience for legitimate users.

🆚 Comparison with Other Detection Methods

User Experience and Intrusiveness

Compared to CAPTCHAs, which actively interrupt the user journey and force an interaction, silent push notifications are completely invisible. This provides a frictionless method of verification. While visible push notifications require a tap, they are often less disruptive than solving a puzzle. The primary dependency is that the user must have opted-in to notifications at some point, a prerequisite not required by CAPTCHAs or IP blocklists.

Detection Accuracy and Evasion

Push notifications offer a higher degree of certainty than IP-based blocklisting alone. An IP address can be shared by many users (both good and bad), but a device push token is unique to an app installation on a specific device. This makes it harder to spoof. However, sophisticated bots running on real devices can be programmed to receive and even interact with notifications, making this method less effective against the most advanced fraud, whereas behavioral analytics might catch subtle anomalies in post-click activity.

Real-Time vs. Batch Processing

Push notification challenges are inherently a real-time mechanism. The validation happens within seconds of the click, allowing for immediate decisions to block or allow traffic. This is a significant advantage over methods that rely on post-campaign analysis or batch processing of log files to identify fraud after the ad budget has already been spent. They integrate seamlessly into real-time bidding (RTB) environments where instant decisions are necessary.

⚠️ Limitations & Drawbacks

While push notifications are a powerful tool for fraud detection, they are not a complete solution and have notable limitations. Their effectiveness depends heavily on user consent and the specific type of fraud being targeted. In some scenarios, relying solely on this method can be inefficient or insufficient.

Opt-In Requirement – The entire method is ineffective if the user has not granted permission for push notifications, rendering a large segment of users unverifiable through this channel.
Platform Dependency – The system relies entirely on third-party services like Apple's APNs and Google's FCM, which can experience delivery delays or outages beyond the control of the fraud detection system.
Limited Scope – This technique is primarily effective for mobile app traffic and web push subscribers. It offers no protection for standard desktop web traffic or users who browse without installing service workers.
Advanced Bot Evasion – Sophisticated bots running on real, compromised devices can be programmed to successfully receive and respond to push challenges, making them appear legitimate to this specific check.
False Negatives – Clicks from users who have disabled notifications or are temporarily offline may be incorrectly flagged as fraudulent due to a lack of response, potentially blocking legitimate traffic.
Not a Standalone Solution – Push validation works best as one layer in a multi-faceted security approach; it cannot detect other fraud types like ad stacking, click injection, or attribution fraud.

Due to these drawbacks, push notification challenges should be combined with other detection strategies like behavioral analysis and IP reputation scoring for a more robust defense.

❓ Frequently Asked Questions

How does a silent push notification help detect fraud?

A silent push notification acts as a background "ping" to a device. It doesn't alert the user but verifies that the device token is active and linked to a real installation of your app. If the push fails, it suggests the token is fake or the app was uninstalled, which is common in bot-driven fraud.

Can push notifications stop all types of click fraud?

No. Push notifications are highly effective at stopping simple to moderately complex bots that operate on emulators or use fake device information. However, they are less effective against sophisticated bots on real devices or other fraud types like click stacking and attribution fraud. They should be part of a multi-layered security strategy.

Is using push notifications for security compliant with privacy regulations like GDPR?

Yes, provided it is handled correctly. Users must have explicitly opted in to receive push notifications. The data collected through the push (like a confirmation ping) should be used solely for the stated purpose of security and fraud prevention and managed according to data protection principles outlined in regulations like GDPR.

Does using push notifications for security negatively affect user experience?

If silent (background) push notifications are used, there is zero impact on the user experience as they are invisible. If actionable notifications are used (e.g., "Confirm Login"), they can be a minor extra step but often increase the user's sense of security, which can be a positive trade-off.

What is the main difference between using push for marketing vs. security?

Marketing pushes are designed to engage the user with compelling content to drive an action like a purchase. Security pushes are designed to validate the user or device. Their goal is not engagement but verification, often through invisible background processes or simple, direct confirmation requests.

🧾 Summary

In digital ad security, push notifications serve as a critical verification layer to combat fraud. By sending visible or silent challenges to a user's device, this method actively confirms the presence of a legitimate human on a real device, rather than a bot on an emulator. It is highly effective for filtering invalid traffic, protecting ad budgets, and ensuring data accuracy by leveraging unique device tokens for validation.