Identifier For Vendors (IDFV)

How Identifier For Vendors IDFV Works

User Device (iOS)
│
├── App A (Vendor X) → Generates/Accesses IDFV: "ABC-123"
│    │
│    └─► Ad Click/Event Data + IDFV Sent to Server
│
├── App B (Vendor X) → Generates/Accesses IDFV: "ABC-123" (Same IDFV)
│    │
│    └─► Ad Click/Event Data + IDFV Sent to Server
│
├── App C (Vendor Y) → Generates/Accesses IDFV: "XYZ-789" (Different IDFV)
│
└─► Server-Side Analysis
    │
    ├─► Collects data from App A & App B
    │
    └─► Compares traffic using shared IDFV "ABC-123"
        │
        └─► Identify suspicious patterns (e.g., high-frequency clicks from one IDFV across multiple apps) → Flag as potential fraud.

The Identifier for Vendors (IDFV) provides a stable way for app developers to recognize a user’s device across all of their applications on that device. Unlike the device-wide Identifier for Advertisers (IDFA), the IDFV is specific to the app vendor. This mechanism is foundational for traffic security systems to detect fraudulent activities originating from a single device across a portfolio of apps. Since the IDFV remains consistent for all apps from the same vendor, it serves as a reliable data point for cross-app analysis and fraud prevention within the vendor’s own ecosystem.

Initial Generation and Persistence

When a user installs the first app from a particular vendor on their iOS device, the operating system generates a unique IDFV. This alphanumeric string is then associated with that vendor’s apps on that specific device. If the user installs other apps from the same vendor, those apps will share the exact same IDFV. This identifier persists as long as at least one app from that vendor remains installed. If all apps from the vendor are uninstalled, the IDFV is deleted. A new IDFV will be generated if the user reinstalls an app from that vendor later.

Data Aggregation for Fraud Analysis

In a traffic security context, each time a user interacts with an ad or makes an in-app action, this event can be logged along with the corresponding IDFV. A developer’s server collects this data from all its apps. By grouping events by the same IDFV, analysts can create a behavioral profile of a device’s activity across the entire app portfolio. This aggregated view is crucial for identifying suspicious patterns that might not be apparent when looking at data from a single app in isolation.

Pattern Recognition and Anomaly Detection

With aggregated data, fraud detection systems can apply rules and algorithms to spot anomalies. For example, an abnormally high number of ad clicks or installs originating from a single IDFV in a short period across multiple apps is a strong indicator of non-human or fraudulent activity. Other patterns, such as clicks from a single IDFV occurring at impossible speeds between different apps, can also be flagged. This allows for more robust fraud prevention than relying on less persistent identifiers. Apple allows the use of IDFV for fraud detection without requiring user consent through the AppTrackingTransparency framework.

Diagram Element Breakdown

User Device (iOS)

This represents the user’s iPhone or iPad where the apps are installed. The entire process begins here, as iOS is responsible for generating and managing the IDFV.

App A, B, C (Vendor X/Y)

These are individual applications installed on the device. Apps A and B are from the same developer (Vendor X) and therefore share the same IDFV (“ABC-123”). App C, from a different developer (Vendor Y), has a separate, unique IDFV (“XYZ-789”). This demonstrates the vendor-specific nature of the identifier.

Server-Side Analysis

This is the backend system where data from all the vendor’s apps is collected and analyzed. It aggregates events based on the shared IDFV. This centralized analysis is where fraudulent patterns are detected by looking at the behavior of a single device across multiple applications from the same vendor.

🧠 Core Detection Logic

Example 1: Cross-App Frequency Capping

This logic prevents ad fatigue and identifies non-human, rapid-fire clicking by limiting the number of times a user (identified by their IDFV) can interact with ads across a single vendor’s portfolio of apps in a given timeframe. It’s a first line of defense against simple bots.

// Define a threshold for maximum clicks per IDFV per hour
MAX_CLICKS_PER_HOUR = 20;

function onAdClick(idfv, timestamp) {
  // Get the current click count for this IDFV in the last hour
  click_count = getRecentClicksForIDFV(idfv, 1_hour);

  if (click_count >= MAX_CLICKS_PER_HOUR) {
    // Flag this click as fraudulent or suspicious
    logFraudulentActivity(idfv, "Exceeded cross-app frequency cap");
    return BLOCK_TRAFFIC;
  } else {
    // Record the valid click
    recordClick(idfv, timestamp);
    return ALLOW_TRAFFIC;
  }
}

Example 2: Behavioral Anomaly Detection

This logic identifies suspicious behavior by analyzing the time between events from the same IDFV across different apps. A human user cannot realistically open, interact with, and switch between multiple apps in a few seconds. This helps detect automated scripts.

// Define a minimum time threshold between actions in different apps
MIN_TIME_BETWEEN_APPS = 10_seconds;

function onAppEvent(idfv, app_id, timestamp) {
  last_event = getLastEventForIDFV(idfv);

  if (last_event && last_event.app_id != app_id) {
    time_diff = timestamp - last_event.timestamp;

    if (time_diff < MIN_TIME_BETWEEN_APPS) {
      logFraudulentActivity(idfv, "Implausible time between cross-app events");
      // This IDFV can be added to a watchlist for further monitoring
      addToWatchlist(idfv);
    }
  }
  // Record the current event
  recordAppEvent(idfv, app_id, timestamp);
}

Example 3: Install Validation

This logic helps prevent app install fraud. If an app install is attributed to a click, the system checks if the IDFV from the click matches the IDFV from the newly installed app. A mismatch could indicate that the install was not a direct result of the ad campaign, or it could be a sign of a more sophisticated attack.

function validateInstall(click_id, install_idfv, install_timestamp) {
  // Retrieve the click data associated with this install
  click_data = getClickData(click_id);

  if (!click_data) {
    logSuspiciousInstall(install_idfv, "Install without a corresponding click");
    return;
  }

  click_idfv = click_data.idfv;

  // Check if the IDFV from the click event matches the install event
  if (click_idfv != install_idfv) {
    logFraudulentActivity(click_idfv, "IDFV mismatch between click and install");
  } else {
    // IDFV matches, the install is likely legitimate from a cross-promotion
    logValidInstall(install_idfv);
  }
}

📈 Practical Use Cases for Businesses

The Identifier for Vendors (IDFV) is a unique, alphanumeric identifier assigned by Apple to all apps from the same developer on a device. It allows businesses to track user activity across their own apps without user consent, which is crucial for fraud detection, cross-promotion, and analytics, especially with the limitations on the IDFA. By using the IDFV, companies can identify suspicious patterns and protect their advertising budgets.

• Cross-Promotional Campaign Attribution: Use IDFV to accurately attribute app installs that originate from ads shown in other apps you own. This ensures you are not paying for organic installs and helps measure the true effectiveness of your internal campaigns.
• Fraudulent User Segmentation: Identify and segment users (IDFVs) that exhibit bot-like behavior across your app portfolio. This allows you to exclude them from future campaigns, ensuring your ad spend is focused on real users and improving campaign ROI.
• Internal Ad Frequency Capping: By tracking an IDFV across your apps, you can control how many times a specific user sees an ad from your network. This prevents ad fatigue, improves the user experience, and stops bots from generating excessive, low-quality impressions.
• Securing In-App Purchases: Monitor purchase behavior tied to a specific IDFV across all your apps. This can help identify users who abuse refund policies or use fraudulent payment methods in one app and attempt to do the same in another.

Example 1: Cross-Promotion Install Validation Rule

// Logic to validate an install coming from a cross-promotional ad
function validateCrossPromoInstall(click_idfv, install_idfv, campaign_type) {
  if (campaign_type === "CROSS_PROMOTION") {
    if (click_idfv === install_idfv) {
      // The install is from the same device that clicked the ad.
      return "VALID_INSTALL";
    } else {
      // The IDFVs do not match, indicating potential install fraud.
      return "FRAUDULENT_INSTALL";
    }
  }
  return "NOT_CROSS_PROMOTION";
}

Example 2: Bot Behavior Scoring Logic

// Pseudocode to score an IDFV based on suspicious cross-app activity
function calculateIdfvFraudScore(idfv) {
  let score = 0;
  const events = getEventsForIdfv(idfv, last_24_hours);

  // High number of events across multiple apps
  if (events.length > 500) {
    score += 30;
  }

  // Time between events is too short
  for (let i = 1; i < events.length; i++) {
    if (events[i].timestamp - events[i-1].timestamp < 2) { // Less than 2 seconds
      score += 5;
    }
  }

  // Same click pattern across different apps
  if (hasRepetitivePattern(events)) {
    score += 40;
  }

  return score; // Higher score means higher fraud probability
}

🐍 Python Code Examples

This Python code demonstrates a simple way to detect abnormal click frequency from the same Identifier for Vendors (IDFV) across a portfolio of apps. It works by tracking the timestamps of clicks for each IDFV and flagging those with an unusually high number of clicks in a short period, which is a common sign of bot activity.

from collections import defaultdict
from datetime import datetime, timedelta

# In-memory store for recent clicks (in a real system, use a database like Redis)
CLICK_EVENTS = defaultdict(list)
TIME_WINDOW = timedelta(minutes=5)
FREQUENCY_THRESHOLD = 15

def record_click(idfv: str, app_id: str):
    """Records a click event for a given IDFV and app."""
    now = datetime.now()
    
    # Clean up old events to save memory
    recent_clicks = [t for t in CLICK_EVENTS[idfv] if now - t < TIME_WINDOW]
    CLICK_EVENTS[idfv] = recent_clicks
    
    # Add the new click
    CLICK_EVENTS[idfv].append(now)
    
    print(f"Click recorded for IDFV: {idfv} from App: {app_id}")

def check_for_fraud(idfv: str) -> bool:
    """Checks if the IDFV has exceeded the click frequency threshold."""
    is_fraudulent = len(CLICK_EVENTS[idfv]) > FREQUENCY_THRESHOLD
    if is_fraudulent:
        print(f"  [!] Fraud Alert: High click frequency detected for IDFV {idfv}")
    return is_fraudulent

# --- Simulation ---
suspicious_idfv = "FRAUD-IDFV-1234"
normal_idfv = "NORMAL-IDFV-5678"

# Simulate a burst of clicks from a suspicious IDFV
for i in range(20):
    record_click(suspicious_idfv, f"App-{i % 3}")
check_for_fraud(suspicious_idfv)

# Simulate normal activity
record_click(normal_idfv, "App-A")
record_click(normal_idfv, "App-B")
check_for_fraud(normal_idfv)

This example shows how to analyze session behavior to identify suspicious users. The code calculates the time difference between consecutive events from the same IDFV across different apps. If the time is unnaturally short, it suggests automated behavior, as a human user cannot switch between and interact with apps that quickly.

# In-memory store for the last event from an IDFV
LAST_EVENT = {}
IMPLAUSIBLE_TIME_SECONDS = 2

def process_app_event(idfv: str, app_id: str):
    """Analyzes time between events to detect suspicious activity."""
    now = datetime.now()
    is_suspicious = False
    
    if idfv in LAST_EVENT:
        last_app, last_time = LAST_EVENT[idfv]
        
        # Check if the event is from a different app
        if app_id != last_app:
            time_diff = (now - last_time).total_seconds()
            if time_diff < IMPLAUSIBLE_TIME_SECONDS:
                print(f"  [!] Fraud Alert: Implausibly fast switch ({time_diff:.2f}s) for IDFV {idfv} between {last_app} and {app_id}")
                is_suspicious = True

    # Update the last event for this IDFV
    LAST_EVENT[idfv] = (app_id, now)
    return is_suspicious

# --- Simulation ---
bot_idfv = "BOT-IDFV-9012"

# Simulate a bot switching and clicking apps rapidly
process_app_event(bot_idfv, "GameApp")
import time
time.sleep(0.5) # Simulate a very short delay
process_app_event(bot_idfv, "SocialApp")

Types of Identifier For Vendors IDFV

• Standard IDFV: This is the default implementation from Apple, providing a consistent identifier for all apps from a single vendor on a user's device. It is the primary method for tracking a device's activity within a developer's own ecosystem without requiring App Tracking Transparency consent.
• Server-Validated IDFV: In this approach, the IDFV collected on the client-side is sent to a server and cross-referenced with other data points (like IP address or user agent) for validation. This helps ensure the IDFV is coming from a legitimate device and hasn't been tampered with or submitted via a fraudulent script.
• IDFV with Session Clustering: This method involves grouping multiple IDFVs that exhibit identical behavioral patterns (e.g., synchronized clicks, similar conversion times across different devices). This can help identify large-scale, coordinated fraud attacks where bots use different devices but are controlled by a single source.
• IDFV with Probabilistic Matching: When an IDFV is unavailable or changes (e.g., after app reinstallation), this technique uses other non-persistent signals like IP address, device model, and OS version to probabilistically link the new IDFV to the old one. This helps maintain a continuous user profile for fraud analysis.

🛡️ Common Detection Techniques

• Cross-App Behavioral Analysis: This technique involves monitoring a user's actions, identified by their IDFV, across all apps from the same vendor. It detects fraud by identifying coordinated, non-human patterns, such as simultaneous clicks or conversions in multiple apps from a single device.
• Frequency Capping and Anomaly Detection: By tracking the number of clicks or installs from a single IDFV within a specific timeframe, this method detects suspicious spikes in activity. An unnaturally high frequency across a vendor's app portfolio often indicates automated bot traffic.
• IDFV Persistence Tracking: This technique flags IDFVs that disappear and reappear with a new value in a short amount of time. This pattern can indicate that a fraudster is repeatedly uninstalling and reinstalling apps to reset the identifier and commit install fraud.
• Geographic and Network Consistency Check: This involves checking if the geographic location and network data (like ISP or IP range) associated with an IDFV's activities are consistent. Sudden, impossible jumps in location for the same IDFV can expose proxy or VPN usage common in ad fraud.

🧰 Popular Tools & Services

Tool	Description	Pros	Cons
Vendor-Side Traffic Analyzer	A service that analyzes traffic across a developer's suite of apps using IDFV to identify suspicious cross-app patterns and synchronized fraudulent behavior.	Effective at catching coordinated fraud within a single vendor's ecosystem; doesn't require IDFA consent.	Limited to a single vendor's apps; ineffective against fraud that spans multiple developers.
Mobile Attribution Platform	Platforms like AppsFlyer or Adjust use IDFV for attributing installs from cross-promotional campaigns and as a stable identifier for analysis when IDFA is unavailable.	Provides a holistic view of campaign performance; integrates easily with advertising networks.	Primary focus is on attribution, not always specialized in advanced fraud detection techniques.
Real-Time Click Filtering API	An API that scores incoming clicks in real-time based on rules applied to the IDFV, such as frequency caps, behavioral heuristics, and anomaly detection.	Blocks fraud before it impacts campaign budgets; highly customizable rules.	Requires technical integration; may have higher latency compared to post-analysis methods.
Device Intelligence SDK	An SDK that combines IDFV with other device signals (like OS version, device model) to create a more robust and persistent device fingerprint for fraud detection.	Enhances detection accuracy by adding more data layers; can help identify fraud even if the IDFV changes.	Increases the complexity of the app; may have privacy implications if not handled correctly.

📊 KPI & Metrics

When deploying fraud detection systems based on the Identifier for Vendors (IDFV), it's crucial to track metrics that measure both technical effectiveness and business impact. Monitoring these key performance indicators (KPIs) ensures that the system is not only accurately identifying fraud but also protecting advertising budgets and improving overall campaign performance without blocking legitimate users.

Metric Name	Description	Business Relevance
Fraudulent IDFV Rate	The percentage of unique IDFVs flagged as fraudulent out of the total IDFVs analyzed.	Indicates the overall level of fraud within the vendor's app ecosystem and the detection system's sensitivity.
False Positive Rate	The percentage of legitimate IDFVs that were incorrectly flagged as fraudulent.	A high rate can lead to blocking real users and lost revenue, impacting user experience and scale.
Blocked Clicks/Installs	The total number of clicks or installs that were blocked due to being associated with a fraudulent IDFV.	Directly measures the volume of fraud prevented and helps quantify the ad spend saved.
Post-Install Conversion Rate of Clean Traffic	The conversion rate (e.g., purchases, sign-ups) of traffic that was not flagged as fraudulent by the IDFV system.	An increase in this metric indicates that the system is successfully filtering out low-quality, non-converting traffic.

These metrics are typically monitored through real-time dashboards that visualize incoming traffic and fraud alerts. Automated alerts can notify analysts of sudden spikes in fraudulent activity or significant changes in key metrics. This continuous feedback loop is essential for tuning fraud detection rules, adapting to new threats, and optimizing the balance between aggressive fraud prevention and minimizing the impact on genuine users.

🆚 Comparison with Other Detection Methods

Detection Accuracy and Scope

The Identifier for Vendors (IDFV) is highly accurate for detecting fraud within a single developer's portfolio of apps, as it provides a stable identifier for a device across that ecosystem. However, its main limitation is its scope; it cannot track users across apps from different vendors. In contrast, signature-based filters can identify known bot signatures across the entire ad network but may miss new or sophisticated threats. Behavioral analytics offers a broader view by analyzing patterns across different vendors but may be less precise than IDFV if it relies on less stable identifiers like IP addresses.

Real-Time vs. Batch Processing

IDFV is well-suited for real-time fraud detection. Because the identifier is readily available on the device, traffic can be scored and filtered as clicks or installs happen. This allows for immediate blocking of fraudulent activity. Signature-based filtering is also very fast and works in real-time. Complex behavioral analytics, on the other hand, often requires large datasets to be analyzed in batches, which can introduce delays in detection and response. This makes it more suitable for post-attribution analysis rather than real-time prevention.

Effectiveness Against Coordinated Fraud

IDFV is particularly effective against coordinated fraud originating from a single device across multiple apps of the same vendor. It can easily spot a device that is generating an unrealistic volume of events. However, it is less effective against large-scale botnets where each device has a unique IDFV. CAPTCHAs can be effective at stopping simple bots on a per-interaction basis but are intrusive to the user experience and can be solved by advanced bots. Behavioral analytics is generally more robust against large-scale, distributed attacks by identifying common patterns across many devices.

⚠️ Limitations & Drawbacks

While the Identifier for Vendors (IDFV) is a valuable tool for click fraud protection, it has inherent limitations that can make it less effective in certain scenarios. Its primary drawback is its limited scope, as it can only track users within a single vendor's ecosystem of apps. This creates blind spots for fraud that occurs across different developers.

• Limited Scope: The IDFV cannot be used to track users across apps from different vendors, making it impossible to detect large-scale fraudulent campaigns that span multiple developer accounts.
• Resets on Reinstall: If a user deletes all apps from a vendor and then reinstalls one, a new IDFV is generated. Fraudsters can exploit this by repeatedly reinstalling apps to reset their identifier and evade detection.
• No Android Equivalent: The IDFV is an Apple-specific feature and has no direct equivalent on Android. This means developers need to use different strategies for fraud detection on each platform, adding complexity.
• Ineffective for Web-Based Fraud: The IDFV is native to mobile apps and provides no visibility into fraudulent activity that originates from web browsers, even if it leads to an app install.
• Vulnerable to Device Emulation: Sophisticated fraudsters using device emulators can generate new, unique IDFVs for each emulated device, making it appear as though traffic is coming from a large number of legitimate users.

Due to these limitations, relying solely on IDFV for fraud detection is insufficient. A more robust approach often requires a hybrid strategy that combines IDFV data with other signals like IP analysis, behavioral modeling, and device fingerprinting.

❓ Frequently Asked Questions

How is IDFV different from IDFA?

The Identifier for Advertisers (IDFA) is a device-wide identifier that tracks users across all apps for advertising purposes, but it requires user consent. The IDFV is vendor-specific, meaning it's the same for all apps from one developer on a device and does not require user consent for analytics or fraud prevention within that vendor's ecosystem.

Does the IDFV change?

Yes, the IDFV for a particular vendor will change if the user deletes all of that vendor's apps from their device and later reinstalls one. This reset mechanism can be exploited by fraudsters to evade tracking and detection systems that rely solely on the IDFV.

Is IDFV available on Android devices?

No, the Identifier for Vendors is an Apple-specific feature and is not available on Android. The Android equivalent for advertising tracking is the Google Advertising ID (GAID), which functions more like Apple's IDFA.

Can IDFV be used for ad attribution?

IDFV is primarily used for attributing installs from cross-promotional campaigns within a vendor's own portfolio of apps. However, it cannot be used for attribution across different vendors' apps, as the identifier will be different. For broader attribution, the industry relies on other methods, especially since the decline of IDFA.

Do I need user consent to use the IDFV for fraud detection?

According to Apple's policies, you do not need to request user permission through the AppTrackingTransparency framework if the IDFV is used solely for purposes like analytics or fraud prevention within your own apps. However, you cannot combine it with other data to track users across apps and websites owned by other companies without consent.

🧾 Summary

The Identifier for Vendors (IDFV) is an Apple-provided ID unique to a developer's apps on a single device. In fraud prevention, it serves as a stable marker to track user behavior and detect anomalies across a developer’s app ecosystem without requiring user consent. By analyzing patterns associated with an IDFV, businesses can identify non-human traffic, prevent duplicate install fraud, and protect advertising budgets, improving campaign integrity.

🔗 Related Articles

• What is the IDFV (identifier for vendors)? - Adjust – https://www.adjust.com/glossary/identifier-for-vendors-idfv/
• What is IDFV? | AppsFlyer mobile glossary – https://www.appsflyer.com/glossary/idfv-identifier-for-vendors/
• What is IDFV? | Identifier for Vendors | IDFV vs IDFA | iOS IDFV - Kochava – https://www.kochava.com/glossary/identifier-for-vendors-idfv/
• Identifier for Vendors (IDFV) - Branch.io – https://branch.io/glossary/idfv/
• User Privacy and Data Use - App Store - Apple Developer – https://developer.apple.com/app-store/user-privacy-and-data-use/