In app bidding

What is In app bidding?

In-app bidding is an automated auction method where mobile publishers offer ad inventory to multiple advertisers at once. Within fraud prevention, this process is important because it generates transparent, real-time bid data. Analyzing this data for anomalies helps identify and block fraudulent activities like bot-driven clicks or fake impressions.

How In app bidding Works

[User Action] β†’ Ad Impression Available β†’ App SDK β†’ [In-App Bidding Auction]
                                                     β”‚
               +─────────────────────────────────────+─────────────────────────────────────+
               β”‚                                     β”‚                                     β”‚
         [Bid Request] β†’ Demand Source A       [Bid Request] β†’ Demand Source B       [Bid Request] β†’ Demand Source C
               β”‚             (Bid: $1.50)            β”‚             (Bid: $0.75)            β”‚             (Bid: $1.65 - Invalid)
               β”‚                                     β”‚                                     β”‚
               ↓                                     ↓                                     ↓
     [Security Analysis]                     [Security Analysis]                     [Security Analysis]
       └─ IP Check: OK                         └─ Behavior Check: OK                    └─ Bot Signature: MATCH
       └─ Geo Check: OK                        └─ History Check: OK                     └─ IP Reputation: BAD
               β”‚                                     β”‚                                     β”‚
            [VALID]                               [VALID]                               [INVALID]
               β”‚                                     β”‚                                     β”‚
               +──────────────────┐                  β”‚                  β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€+
                                  β”‚                  β”‚                  β”‚
                                  └─────→ [Auction Logic] β†β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜
                                              β”‚
                                              ↓
                                        [Highest Valid Bid Wins] β†’ Ad Served (Source A)
In-app bidding is a method where mobile app publishers can auction their ad inventory to many advertisers simultaneously in real-time. From a traffic security perspective, this unified auction model provides a rich stream of data that is crucial for identifying and preventing ad fraud. Instead of relying on sequential, predetermined ad network calls (the “waterfall” method), bidding creates a competitive and transparent environment. Every bid request and response for an impression can be scrutinized before the winning ad is chosen and displayed. This allows security systems to analyze patterns, validate sources, and block malicious actors in milliseconds, ensuring that advertisers are paying for legitimate human views, not fraudulent activity generated by bots. The entire process is automated through an SDK integrated into the app, which manages the communication and auction process with various demand partners.

Initiation and Bid Request

When an ad opportunity becomes available in an app (e.g., a user reaches a level in a game where a rewarded video can be shown), the app’s integrated SDK initiates an ad request. This request is not sent to one ad network at a time, but broadcast simultaneously to multiple demand sources, including ad exchanges and demand-side platforms (DSPs). Each request contains data about the ad placement, the app, and non-personal user information. For fraud detection, this initial step is critical as it marks the beginning of a transaction that can be monitored for legitimacy, ensuring the request originates from a real device and valid app version.

Real-Time Auction and Security Scrutiny

Upon receiving the bid request, demand sources interested in the impression submit their bids in real-time. This is where fraud detection systems play a pivotal role. Before a bid is accepted into the final auction, it is analyzed against various security parameters. This can include checking the bidder’s IP address against known data center or proxy lists, analyzing the user agent for signs of emulation, and cross-referencing the device ID against a reputation database. Machine learning models can also identify anomalies in bidding patterns that suggest non-human behavior, such as impossibly fast bid responses or bids from geographically inconsistent locations.

Winning Bid and Ad Display

After invalid bids are filtered out, the highest valid bid wins the auction. The winning advertiser’s ad creative is then delivered by the SDK to be displayed to the user. This final step is also monitored; security tools can perform post-bid analysis to ensure that the ad renders correctly and that there is no malicious activity like ad stacking (where multiple ads are layered in a single slot) or hidden ads. This continuous monitoring protects both the advertiser from wasted spend and the user from potential malware, preserving the integrity of the advertising ecosystem.

Diagram Element Breakdown

[User Action] β†’ Ad Impression Available

This represents the trigger within the app that creates an ad opportunity. It is the starting point of the process. In fraud detection, ensuring this action is initiated by a genuine user, not a script, is the first line of defense.

[In-App Bidding Auction]

This is the central hub where all demand sources compete simultaneously. Its transparency is key for security, as it allows for a holistic view of all participants, making it easier to spot collusive or fraudulent bidding patterns that would be hidden in a sequential waterfall system.

[Bid Request] β†’ Demand Source

This shows the app’s SDK sending out the call for bids to various advertisers. The data within these requests is analyzed to ensure it hasn’t been tampered with, such as through app spoofing, where a low-quality app pretends to be a premium one.

[Security Analysis]

This is the core of fraud prevention within the bidding flow. Each bid is individually inspected for signs of invalid traffic (IVT). Checks like IP reputation, bot signatures, and behavioral anomalies determine if a bid is legitimate before it is allowed to compete.

[VALID] / [INVALID]

This represents the outcome of the security analysis. Bids flagged as invalid are discarded from the auction. This step actively prevents ad spend from being wasted on fraudulent sources like data centers or known botnets.

[Auction Logic] β†’ [Highest Valid Bid Wins]

This is the final stage where the highest-priced bid from the pool of validated participants wins the right to serve the ad. This ensures publishers get the best price for their inventory while advertisers are protected from competing with fraudulent, artificially-priced bids.

🧠 Core Detection Logic

Example 1: Bid Request Velocity Analysis

This logic identifies non-human behavior by tracking the frequency of bid requests from a single device ID. An impossibly high number of requests in a short period indicates that a bot or script is operating the device, not a human. It is applied pre-bid to filter out suspicious devices before the auction.

FUNCTION check_request_velocity(device_id, request_timestamp):
  // Retrieve past request timestamps for the device_id
  request_history = get_request_history(device_id)

  // Count requests in the last 60 seconds
  recent_requests = count(t for t in request_history if now() - t < 60)

  IF recent_requests > 20: // Threshold for abnormal frequency
    FLAG as "Suspicious: High Request Velocity"
    RETURN INVALID
  ELSE:
    // Add current request to history
    add_to_history(device_id, request_timestamp)
    RETURN VALID

Example 2: App Bundle ID Spoofing Detection

This logic prevents a common type of fraud where a low-quality app pretends to be a high-value, popular app to attract higher bids. It cross-references the app’s claimed Bundle ID with a known, verified list of app store IDs. This check ensures the bid request is coming from a legitimate, correctly identified application.

FUNCTION verify_bundle_id(claimed_bundle_id, device_os):
  // Get verified list of bundle IDs from official app stores
  verified_list_ios = get_app_store_ids("iOS")
  verified_list_android = get_play_store_ids("Android")

  IF device_os == "iOS":
    IF claimed_bundle_id NOT IN verified_list_ios:
      FLAG as "Fraud: Spoofed Bundle ID"
      RETURN INVALID
  ELSE IF device_os == "Android":
    IF claimed_bundle_id NOT IN verified_list_android:
      FLAG as "Fraud: Spoofed Bundle ID"
      RETURN INVALID
  ELSE:
    RETURN VALID

Example 3: Geolocation Mismatch Detection

This logic identifies fraud by comparing the IP address location from the bid request with the device’s self-reported GPS location (if available and consented). A significant mismatch between the two can indicate the use of a proxy or VPN to fake a more valuable location, or that the IP address belongs to a data center.

FUNCTION check_geo_mismatch(ip_address, device_gps_coords):
  // Get geographic location from IP address
  ip_location = get_geo_from_ip(ip_address)

  // Get geographic location from GPS coordinates
  gps_location = get_geo_from_coords(device_gps_coords)

  // Calculate distance between the two locations
  distance = calculate_distance(ip_location, gps_location)

  IF distance > 100: // Set a reasonable threshold in kilometers
    FLAG as "Suspicious: Geo Mismatch"
    RETURN INVALID
  ELSE:
    RETURN VALID

πŸ“ˆ Practical Use Cases for Businesses

For businesses, in-app bidding’s data transparency is a powerful tool against ad fraud. By analyzing real-time bid streams, companies can protect their advertising budgets, ensure campaign data is clean, and improve return on ad spend (ROAS). The auction mechanics allow for pre-bid filtering, where traffic is vetted before a purchase is made, preventing wasted spend on fraudulent impressions generated by bots or other invalid sources. This leads to more accurate performance metrics and better-informed strategic decisions.

  • Campaign Shielding – Protects active campaigns by using real-time bid data to identify and block traffic from known fraudulent sources like data centers or botnets before ad spend is committed.
  • Performance Integrity – Ensures marketing analytics are based on real human interactions, not inflated by invalid clicks or impressions. This leads to more reliable key performance indicators (KPIs) and accurate ROAS calculations.
  • Budget Optimization – Prevents ad budgets from being wasted on non-viewable or fraudulent inventory. By filtering out invalid traffic pre-bid, ad spend is automatically allocated toward legitimate, high-quality impressions that have a chance of converting.
  • Supply Path Auditing – Provides transparency into the ad supply chain, allowing businesses to evaluate the quality of traffic from different publishers and ad exchanges, and to blacklist those with high rates of invalid traffic.

Example 1: Data Center IP Filtering Rule

This logic prevents bids originating from servers in data centers, which are a common source of non-human traffic. It checks the bid request’s IP against a known list of data center IP ranges. This is a fundamental pre-bid check to eliminate obvious bot traffic.

FUNCTION block_datacenter_traffic(bid_request):
  ip = bid_request.ip_address
  datacenter_ip_list = get_known_datacenter_ips()

  IF ip IN datacenter_ip_list:
    REJECT_BID("Source identified as data center")
    RETURN FALSE
  ELSE:
    ACCEPT_BID()
    RETURN TRUE

Example 2: Session Scoring for Engagement Fraud

This logic scores user sessions to detect sophisticated invalid traffic (SIVT) where a bot might mimic some human behavior. It analyzes a combination of signals from the bid requestβ€”like time-to-install, click frequency, and device propertiesβ€”to assign a fraud score. Bids from sessions with scores above a certain threshold are blocked.

FUNCTION score_session_validity(bid_request):
  score = 0
  
  // Rule 1: Abnormally fast click-to-install time
  IF bid_request.ctit < 10 seconds:
    score += 40

  // Rule 2: Multiple rapid clicks from same device
  IF bid_request.click_frequency > 5 in last minute:
    score += 30

  // Rule 3: Mismatched device language and timezone
  IF bid_request.device_language != bid_request.timezone_inferred_language:
    score += 15

  // Rule 4: Outdated or unusual OS version
  IF bid_request.os_version is_known_compromised:
    score += 15

  IF score > 50: // Threshold for blocking
    REJECT_BID("Session score exceeds fraud threshold")
    RETURN "INVALID"
  ELSE:
    RETURN "VALID"

🐍 Python Code Examples

This Python function demonstrates a basic way to filter out bid requests coming from known fraudulent IP addresses, such as those associated with data centers or public proxies. By checking each incoming IP against a blocklist, businesses can perform a simple yet effective pre-bid check to reject obviously non-human traffic.

# A blocklist of known fraudulent IP addresses
FRAUDULENT_IPS = {"198.51.100.5", "203.0.113.10", "192.0.2.14"}

def filter_suspicious_ips(bid_request):
    """
    Checks if a bid request's IP is in a blocklist.
    """
    ip_address = bid_request.get("ip")
    if ip_address in FRAUDULENT_IPS:
        print(f"Blocking bid from suspicious IP: {ip_address}")
        return None
    
    print(f"Accepting bid from IP: {ip_address}")
    return bid_request

# Simulate incoming bid requests
bid1 = {"id": "xyz-123", "ip": "8.8.8.8"}
bid2 = {"id": "abc-456", "ip": "203.0.113.10"}

filter_suspicious_ips(bid1)
filter_suspicious_ips(bid2)

This example simulates the detection of click injection, a type of mobile ad fraud where malware on a device tries to claim credit for an app install. The code checks the time between a click and the subsequent install; an abnormally short time indicates that a script, not a user, likely triggered the install immediately after detecting a download.

import datetime

def detect_click_injection(click_timestamp, install_timestamp):
    """
    Analyzes the time between a click and an install (CTIT).
    A very short duration can indicate fraud.
    """
    time_delta = install_timestamp - click_timestamp
    
    # If install happens less than 10 seconds after the click, it's suspicious
    if time_delta.total_seconds() < 10:
        print(f"Fraud Alert: Click injection suspected. CTIT: {time_delta.total_seconds()}s")
        return True
        
    print(f"Valid Install: CTIT of {time_delta.total_seconds()}s is acceptable.")
    return False

# Simulate event timestamps
now = datetime.datetime.now()
click_time_valid = now - datetime.timedelta(minutes=5)
click_time_fraud = now - datetime.timedelta(seconds=3)
install_time = now

detect_click_injection(click_time_valid, install_time)
detect_click_injection(click_time_fraud, install_time)

This code analyzes user agent strings from bid requests to identify traffic from bots or emulators instead of genuine mobile devices. It flags requests that contain common bot signatures or lack standard mobile browser identifiers, helping to filter out non-human traffic sources.

def analyze_user_agent(bid_request):
    """
    Inspects the user agent string for signs of bots or emulators.
    """
    user_agent = bid_request.get("user_agent", "").lower()
    
    # Common bot/crawler signatures
    bot_signatures = ["bot", "crawler", "spider", "headlesschrome"]
    
    # Standard mobile identifiers
    mobile_identifiers = ["iphone", "android", "mobile"]
    
    is_bot = any(sig in user_agent for sig in bot_signatures)
    is_mobile = any(iden in user_agent for iden in mobile_identifiers)

    if is_bot or not is_mobile:
        print(f"Flagging suspicious user agent: {user_agent}")
        return False
        
    print(f"Valid mobile user agent: {user_agent}")
    return True

# Simulate incoming bid requests
bid_real_user = {"user_agent": "Mozilla/5.0 (iPhone; CPU iPhone OS 15_5 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/15.5 Mobile/15E148 Safari/604.1"}
bid_bot = {"user_agent": "Mozilla/5.0 (compatible; Googlebot/2.1; +http://www.google.com/bot.html)"}
bid_emulator = {"user_agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) HeadlessChrome/103.0.5060.114 Safari/537.36"}

analyze_user_agent(bid_real_user)
analyze_user_agent(bid_bot)
analyze_user_agent(bid_emulator)

Types of In app bidding

  • Pre-Bid Filtering – This is a real-time defense where incoming bid requests are scanned for fraudulent signals before they enter the auction. It blocks traffic from known bad IPs, suspicious devices, or non-compliant apps, preventing advertisers from ever bidding on invalid inventory.
  • Post-Bid Analysis – This method analyzes impression-level data after an ad has been won and served. It detects anomalies like ad stacking, hidden ads, or abnormal click patterns. While it doesn't prevent the initial spend, it provides data to blacklist fraudulent publishers and claim refunds.
  • Hybrid Bidding Model – This approach combines in-app bidding with traditional waterfall setups. A bidding auction runs first, and if the winning bid doesn't meet a certain price floor, the ad request then proceeds down a waterfall of ad networks. For fraud, this can add complexity but allows for layered security checks.
  • Unified Auction – This is the purest form of in-app bidding, where all demand sources (ad networks, exchanges, DSPs) bid simultaneously in a single, flat auction. From a security standpoint, this type offers maximum transparency, as all bidders and their bid prices are visible at once, making it easier to spot collusion or widespread fraud patterns.

πŸ›‘οΈ Common Detection Techniques

  • IP Reputation Analysis – This technique involves checking the IP address of a bid request against global blacklists of known data centers, VPNs, and proxies. It is a fundamental method for filtering out non-human traffic originating from servers rather than residential devices.
  • Device Fingerprinting – Analyzes a combination of device parameters (OS version, screen size, user agent, language settings) to create a unique identifier. This helps detect when a single device is trying to appear as many different users or when a bot is emulating a device.
  • Behavioral Analysis – This technique monitors user interaction patterns within an app session. It flags anomalies such as impossibly fast clicks, no post-click activity, or repetitive, non-random navigation, which are strong indicators of bot automation rather than genuine human engagement.
  • Click Timestamp Analysis (CTIT) – Measures the time between the ad click and the app installation or first open. An abnormally short duration (e.g., under 10 seconds) is a strong indicator of click injection fraud, where malware on the device programmatically generates a click just before an install completes.
  • Bundle ID and App Spoofing Detection – Verifies that the app's bundle ID in the bid request matches a legitimate, registered app on the official app store. This prevents fraudsters from masquerading as high-quality apps to steal higher ad revenue, a practice known as domain or app spoofing.

🧰 Popular Tools & Services

Tool Description Pros Cons
TrafficGuard A comprehensive fraud prevention solution that offers multi-layered protection by detecting and blocking invalid traffic across the entire user journey, from pre-bid to post-install analysis. Offers real-time click-level protection, attribution validation, and protects a wide range of platforms including mobile and programmatic channels. Requires integration and may need alignment between marketing and sales teams to fully leverage lead quality insights.
ClickCease Specializes in protecting PPC campaigns from click fraud by automatically detecting and blocking fraudulent clicks in real-time. It provides detailed reporting on every click. Integrates directly with Google Ads and Bing Ads, offers competitor monitoring, and uses session recordings to analyze visitor behavior for fraud signals. Primarily focused on click fraud for search and social campaigns, may be less comprehensive for other types of in-app or video ad fraud.
Integral Ad Science (IAS) Provides a suite of ad verification services including fraud detection, viewability, and brand safety. It uses AI and machine learning to identify fraud patterns in mobile campaigns. Offers comprehensive coverage for programmatic buys, customizable reporting, and both pre-bid prevention and post-bid optimization solutions. Can be a complex, enterprise-level solution that may be more than what smaller businesses need.
HUMAN Security (formerly White Ops) A cybersecurity company that specializes in distinguishing human from bot interactions. It protects against sophisticated bot attacks, including SIVT, across various platforms. Effective against sophisticated invalid traffic (SIVT), provides detailed threat categorization, and helps pinpoint blocked pre-bid IVT over time. As a specialized bot mitigation service, its focus is primarily on the bot-human verification layer rather than a broader suite of ad campaign management tools.

πŸ“Š KPI & Metrics

When deploying in-app bidding for fraud protection, it's vital to track metrics that measure both the accuracy of the detection system and its impact on business goals. Monitoring technical KPIs like the invalid traffic (IVT) rate ensures the system is working correctly, while business-outcome metrics like ROAS and CPA demonstrate its financial value and contribution to campaign efficiency.

Metric Name Description Business Relevance
Invalid Traffic (IVT) Rate The percentage of total traffic identified as fraudulent or non-human. A primary indicator of overall traffic quality and the effectiveness of fraud filters.
Click-to-Install Time (CTIT) The average time between a user clicking an ad and installing the app. Helps identify install hijacking and click injection fraud, protecting user acquisition budgets.
Cost Per Acquisition (CPA) The total cost of acquiring a new customer from a specific campaign. Lowering CPA by eliminating fraudulent clicks and installs directly improves marketing efficiency.
Return on Ad Spend (ROAS) The amount of revenue generated for every dollar spent on advertising. Measures the ultimate profitability of campaigns by ensuring ad spend is directed at real users.
False Positive Rate The percentage of legitimate traffic incorrectly flagged as fraudulent by the system. Ensures that fraud filters are not overly aggressive and blocking potential customers.

These metrics are typically monitored through real-time dashboards provided by fraud detection platforms or integrated into a business's internal analytics systems. Alerts are often configured to flag sudden spikes in IVT or unusual changes in performance KPIs. This feedback loop is crucial for optimizing fraud filters and adjusting bidding strategies to adapt to new threats while maximizing campaign reach and effectiveness.

πŸ†š Comparison with Other Detection Methods

Real-Time vs. Batch Processing

In-app bidding’s fraud detection operates in real-time, analyzing and blocking threats pre-bid, before an advertiser’s money is spent. This is a significant advantage over post-campaign batch analysis, where fraud is often discovered long after the budget has been wasted. While batch processing can uncover sophisticated, slow-moving fraud schemes, the pre-bid nature of bidding provides an immediate defense that preserves ad spend and maintains data integrity from the start.

Behavioral Analytics vs. Signature-Based Filtering

Signature-based filtering, like blocking known bad IPs, is a core component of in-app bidding security. It is fast and effective against common, known threats (General Invalid Traffic or GIVT). However, it can be less effective against new or sophisticated bots (SIVT). This is where behavioral analytics, also used in bidding systems, has an edge. By analyzing patterns of interaction, behavioral models can detect previously unseen threats. The most robust systems combine both, using signatures for speed and behavioral analysis for advanced threats.

Scalability and Integration

Compared to manual review or methods that require heavy human intervention, the automated nature of in-app bidding fraud detection is highly scalable. It can process millions of bid requests per second. However, its integration requires technical effort, as an SDK must be incorporated into the app and configured to communicate with demand partners. This initial setup can be more complex than simply applying a post-campaign analysis tool, but it provides a more integrated and proactive defense.

⚠️ Limitations & Drawbacks

While powerful, in-app bidding as a fraud detection mechanism is not without its challenges. Its effectiveness depends heavily on the quality of data signals, the sophistication of the detection algorithms, and the ever-evolving nature of fraudulent tactics. Certain types of fraud can be difficult to detect in the milliseconds available during a real-time auction.

  • SDK Spoofing Vulnerability – Sophisticated fraudsters can sometimes manipulate or spoof the SDK itself, sending fraudulent bid requests that appear legitimate and bypassing initial checks.
  • Latency-Accuracy Trade-off – The need for extremely low-latency auctions means fraud checks must be completed in milliseconds, which may not be enough time to detect complex, sophisticated invalid traffic (SIVT).
  • False Positives – Overly aggressive fraud filters can incorrectly block legitimate human users whose behavior mimics that of bots (e.g., fast clicking in a game), leading to lost revenue opportunities.
  • Encrypted Traffic Blind Spots – As more traffic becomes encrypted for privacy, it can be harder for third-party verification tools to analyze some data signals, potentially allowing certain types of fraud to go undetected.
  • Adversarial Adaptation – Fraudsters constantly adapt their techniques. A detection method that works today might be obsolete tomorrow, requiring continuous updates and investment in machine learning models to keep pace.

In scenarios involving highly sophisticated or slow-developing fraud, a hybrid approach that combines real-time bidding analysis with post-campaign batch processing is often more suitable.

❓ Frequently Asked Questions

How does in-app bidding improve on waterfall monetization for fraud prevention?

In-app bidding offers greater transparency by allowing all advertisers to bid at once in a unified auction. This makes it easier to spot and block suspicious patterns across the entire pool of demand, whereas the sequential, opaque nature of the waterfall model can hide fraudulent sources within individual ad networks.

Can in-app bidding stop all types of ad fraud?

No, while highly effective against many types of invalid traffic like bots and data center traffic, it has limitations. Sophisticated fraud like SDK spoofing or certain behavioral anomalies can be challenging to detect in the real-time, low-latency environment of a bid auction. A multi-layered security approach is still necessary.

Does using in-app bidding increase app latency for the user?

Modern in-app bidding SDKs are designed to be highly efficient and run auctions in milliseconds, often in the background before an ad slot is even visible. While there is some processing overhead, it is generally less than the cumulative latency caused by a long waterfall calling multiple networks sequentially.

What is the difference between General Invalid Traffic (GIVT) and Sophisticated Invalid Traffic (SIVT) in bidding?

General Invalid Traffic (GIVT) is easier to detect and includes things like known bots and data center traffic, which can be filtered using standard lists. Sophisticated Invalid Traffic (SIVT) is designed to mimic human behavior to evade detection and requires more advanced analysis, such as machine learning and behavioral modeling, to identify.

Do I still need a separate ad fraud tool if my mediation platform supports in-app bidding?

Yes, it is highly recommended. While mediation platforms provide the auction framework, specialized ad fraud tools offer more advanced and dedicated detection capabilities. They provide an independent layer of verification and often use more sophisticated algorithms and larger data sets to identify and block threats that a standard platform might miss.

🧾 Summary

In the context of fraud prevention, in-app bidding is a real-time auction method that provides critical transparency into the ad-buying process. By analyzing simultaneous bids from all advertisers, security systems can identify and block fraudulent traffic from bots and other invalid sources before an ad is purchased. This proactive filtering protects advertising budgets, ensures data accuracy, and improves campaign integrity.