Emulated devices

What is Emulated devices?

An emulated device is software that mimics the hardware and operating system of a physical device, like a smartphone, on a computer. In digital advertising, fraudsters use emulators to automate clicks, installs, and other interactions with ads, creating fake traffic to steal advertising budgets meant for real users.

How Emulated devices Works

+------------------+     +--------------------+     +------------------+
| Fraudster        | --> | Emulator           | --> | Ad Network       |
| (Initiates Fraud)|     | (Mimics Device &   |     | (Serves Ad)      |
|                  |     |  Automates Clicks) |     |                  |
+------------------+     +---------+----------+     +--------+---------+
                                   |                         |
                                   | Fake Interaction Data   | Ad Impression
                                   | (Click, Install)        |
                                   v                         v
+----------------------------------+-------------------------+---------+
| Traffic Protection System                                            |
|                                                                      |
| └─> Data Analysis (IP, User Agent, Behavior, Device Properties)      |
|                                                                      |
| └─> Anomaly Detection (Finds non-human patterns, inconsistencies)    |
|                                                                      |
| └─> Flag & Block (Identifies emulator traffic as fraudulent)         |
|                                                                      |
+----------------------------------------------------------------------+
Emulated devices are a primary tool for committing ad fraud by generating large volumes of fake traffic that appears to come from legitimate users. Fraudsters use this technology to programmatically interact with ads—simulating clicks, app installs, and in-app events—to illegitimately claim payouts from advertisers. The process is scalable and allows a single operator to mimic thousands of unique devices from a server, often located in a data center. A traffic protection system works by scrutinizing the data signatures from incoming traffic to differentiate between genuine human users and these automated emulators.

Emulation of Device Properties

An emulator creates a virtual instance of a mobile device, replicating its most common identifiers. This includes the device model, operating system version, screen resolution, and user agent string. Fraudsters configure these properties to mimic a wide range of popular devices, attempting to blend in with legitimate traffic. However, these emulated properties often contain subtle inconsistencies or generic values that advanced detection systems can identify as fraudulent signatures.

Generation of Non-Human Behavior

Once the device is emulated, fraudsters use scripts to automate interactions with ads. These scripts can perform actions at a speed and frequency impossible for a human, such as clicking thousands of ads per minute or installing and immediately uninstalling an app. The behavior is often repetitive and lacks the natural variations seen in genuine user activity, such as organic mouse movements, varied session times, and realistic engagement with app content.

Signal Analysis and Anomaly Detection

A traffic security system intercepts and analyzes data points from every interaction. It cross-references signals to find anomalies that point to emulation. For example, it may detect a mismatch between the device’s IP address (often a data center) and its supposed GPS location. It also looks for the absence of sensor data from accelerometers or gyroscopes, which are present in physical mobile devices but not in emulators. By analyzing these signals collectively, the system can flag and block the fraudulent traffic.

ASCII Diagram Breakdown

Fraudster & Emulator

The process begins with a fraudster who uses an emulator—software that simulates a mobile device. The emulator is the core tool used to generate fake ad interactions automatically. This setup allows for scalable fraud, as one person can control thousands of “devices.”

Ad Network & Interaction Data

The emulator sends fraudulent interaction data, such as clicks or installs, to the ad network. The ad network, unaware of the fraud, serves ads and records these interactions as if they were from genuine users. This is the point where advertising budgets are initially wasted.

Traffic Protection System

This is the defense layer. It intercepts all traffic data and performs deep analysis. It checks for anomalies like data center IPs, inconsistent device properties, and robotic behavior patterns. By identifying these red flags, it can distinguish emulators from real users and block the fraudulent activity before it corrupts analytics or depletes the ad budget.

🧠 Core Detection Logic

Example 1: Inconsistent Device Fingerprint

This logic checks for contradictions in the device’s reported attributes. Emulators often fail to create a perfectly consistent profile, leaving behind clues. For example, a device might report itself as an iPhone but have technical properties (like a WebGL renderer) exclusive to Android emulators. This check is fundamental in identifying spoofed devices.

FUNCTION checkDeviceFingerprint(request):
  device_properties = request.getDeviceProperties()
  user_agent = device_properties.getUserAgent()
  renderer = device_properties.getWebGLRenderer()

  // Known emulator signature
  IF "Google SwiftShader" IN renderer:
    RETURN "FRAUD"

  // Contradictory properties
  IF "iPhone" IN user_agent AND "Android" IN device_properties.getOS():
    RETURN "FRAUD"

  // Missing essential hardware properties
  IF device_properties.hasAccelerometer() == FALSE:
    RETURN "POTENTIAL_FRAUD"

  RETURN "LEGITIMATE"

Example 2: Behavioral Heuristics

This logic analyzes the timing and frequency of user actions during a session. Emulators controlled by scripts often perform actions with unnatural speed and regularity. This rule flags traffic that shows impossibly short intervals between a click and an app install or multiple clicks occurring faster than a human can manage.

FUNCTION checkBehavior(session):
  click_timestamp = session.getClickTime()
  install_timestamp = session.getInstallTime()
  time_to_install = install_timestamp - click_timestamp

  // Flag installs happening too quickly after a click
  IF time_to_install < 2 SECONDS:
    RETURN "FRAUD"

  click_count = session.getClickCount(within_last_minute)
  
  // Flag abnormally high click frequency from one source
  IF click_count > 30:
    RETURN "FRAUD"
  
  RETURN "LEGITIMATE"

Example 3: Sensor Data Validation

This logic verifies the presence of data from physical sensors commonly found in mobile devices. Emulators cannot replicate authentic sensor data from accelerometers, gyroscopes, or proximity sensors. The absence of this data, or the presence of perfectly uniform and predictable data, is a strong indicator of a non-physical, emulated device.

FUNCTION checkSensorData(request):
  device_sensors = request.getSensorData()

  // Emulators typically lack access to these hardware sensors
  IF device_sensors.has("accelerometer") == FALSE AND device_sensors.has("gyroscope") == FALSE:
    RETURN "FRAUD"

  // Check for unnatural, static sensor values
  accelerometer_data = device_sensors.get("accelerometer_events")
  IF accelerometer_data.isStatic() OR accelerometer_data.isEmpty():
    RETURN "POTENTIAL_FRAUD"

  RETURN "LEGITIMATE"

📈 Practical Use Cases for Businesses

  • Campaign Shielding: Protect advertising budgets by proactively blocking clicks and installs from known emulators, ensuring that ad spend reaches real potential customers, not fraudulent bots.
  • Data Integrity: Ensure marketing analytics and user acquisition data are clean and accurate. By filtering out emulator traffic, businesses can make better strategic decisions based on real user behavior and campaign performance.
  • ROAS Improvement: Improve Return On Ad Spend (ROAS) by eliminating wasteful spending on fraudulent interactions. This leads to lower customer acquisition costs and higher conversion rates from genuine traffic sources.
  • Publisher Quality Control: For ad networks, identifying emulated traffic helps in vetting and removing fraudulent publishers from their platform, maintaining the network’s integrity and value for advertisers.

Example 1: IP and Geolocation Mismatch Rule

// Use Case: Filter out traffic where the IP address location (likely a data center)
// does not match the device's reported language or timezone.

FUNCTION validateGeo(request):
  ip_info = getIPInfo(request.ip) // Returns {country, type}
  device_info = request.getDeviceProperties() // Returns {language, timezone}

  // Block known data center traffic
  IF ip_info.type == "DATA_CENTER":
    RETURN "BLOCK"

  // Flag inconsistencies between IP country and device language
  IF ip_info.country == "Vietnam" AND device_info.language == "en-US":
    RETURN "FLAG_FOR_REVIEW"
  
  RETURN "ALLOW"

Example 2: Session Fraud Scoring

// Use Case: Score each user session based on multiple risk factors.
// Sessions exceeding a certain score are blocked automatically.

FUNCTION getFraudScore(session):
  score = 0
  
  IF session.isFromKnownEmulator():
    score += 50
  
  IF session.ip.isDataCenterIP():
    score += 20
    
  IF session.behavior.isRobotic(): // e.g., clicks too fast
    score += 15
    
  IF session.device.hasSensorMismatch():
    score += 15

  RETURN score

// Implementation
user_session = getCurrentSession()
fraud_score = getFraudScore(user_session)

IF fraud_score >= 50:
  blockRequest()
ELSE:
  allowRequest()

🐍 Python Code Examples

This function checks if a given user agent string contains keywords commonly associated with Android emulators. It provides a simple, signature-based method for filtering out traffic from known developer tools that are often repurposed for ad fraud.

def is_known_emulator(user_agent):
    """
    Checks for common emulator footprints in a user agent string.
    """
    emulator_signatures = ["Genymotion", "sdk_gphone", "BlueStacks", "NoxPlayer"]
    for signature in emulator_signatures:
        if signature in user_agent:
            return True
    return False

# Example usage:
ua_string = "Mozilla/5.0 (Linux; Android 10; sdk_gphone_x86) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/86.0.4240.198 Mobile Safari/537.36"
if is_known_emulator(ua_string):
    print("Emulator detected.")

This function analyzes a list of click timestamps from a single user or IP to detect unnaturally frequent clicks. By setting a minimum time threshold between clicks, it can effectively identify and flag automated scripts designed to generate high volumes of fraudulent clicks.

import time

def has_abnormal_click_frequency(click_timestamps, time_window_sec=10, max_clicks=5):
    """
    Detects if more than `max_clicks` occurred within a `time_window_sec`.
    `click_timestamps` should be a list of sorted unix timestamps.
    """
    if len(click_timestamps) < max_clicks:
        return False

    # Check the time difference between a click and the click `max_clicks-1` positions before it
    for i in range(len(click_timestamps) - max_clicks + 1):
        if click_timestamps[i + max_clicks - 1] - click_timestamps[i] < time_window_sec:
            return True
            
    return False

# Example usage:
clicks =
if has_abnormal_click_frequency(clicks):
    print("Fraudulent click frequency detected.")

Types of Emulated devices

  • Standard SDK Emulators: These are official software tools, such as those from Android Studio or Xcode, designed for developers to test apps. Fraudsters abuse these legitimate tools by running them on servers to generate fake traffic, often leaving behind recognizable software fingerprints.
  • Custom-Built Emulators: More sophisticated fraudsters use custom-developed emulators designed specifically to avoid detection. These tools are engineered to better mimic real device hardware and software properties, making them harder to identify than standard, off-the-shelf emulators.
  • Headless Browsers: While not full device emulators, headless browsers (like Puppeteer or Selenium) can be scripted to simulate mobile browsers and user interactions. They are often used for simpler click fraud schemes where a full device OS simulation is not required.
  • Device Spoofing: This technique involves altering device parameters within data packets sent to the ad server to impersonate a legitimate device, without actually emulating the entire device. It's a lightweight form of fraud used to disguise the true origin of traffic, often coming from servers.

🛡️ Common Detection Techniques

  • Device Fingerprinting: This technique analyzes a combination of device attributes (OS, browser, hardware) to create a unique ID. Emulators often have generic or inconsistent fingerprints that do not match known real devices, making them stand out.
  • Behavioral Analysis: Systems monitor user interaction patterns, such as click speed, session duration, and on-page events. Automated scripts running on emulators produce robotic, predictable behaviors that are distinguishable from the natural, variable actions of human users.
  • Sensor Data Analysis: This method checks for the presence and realistic fluctuations of data from mobile sensors like the accelerometer and gyroscope. Emulators cannot generate authentic sensor data, so its absence or perfect uniformity is a strong indicator of fraud.
  • Network and IP Analysis: This involves examining the traffic's source, such as the IP address and internet service provider. Traffic originating from known data centers or servers, rather than residential or mobile networks, is highly indicative of emulator activity.
  • Signature Matching: Fraud detection systems maintain databases of known signatures and properties associated with emulators (e.g., specific hardware names like "goldfish"). Incoming traffic is checked against this database to quickly identify and block known fraudulent sources.

🧰 Popular Tools & Services

Tool Description Pros Cons
Traffic Sentinel Platform A comprehensive suite that uses machine learning to analyze traffic patterns, device fingerprints, and user behavior to detect and block emulator-driven fraud in real-time. High accuracy in detecting sophisticated threats; real-time blocking capabilities; provides detailed reporting for analysis. Can be expensive for small businesses; requires integration and may have a steep learning curve.
ClickGuard API A developer-focused API that provides risk scores for clicks and installs based on IP reputation, device integrity checks, and known fraud signatures. Highly customizable; easy to integrate into existing systems; pay-as-you-go pricing model is flexible. Less effective against behavioral fraud without additional logic; relies heavily on signature-based detection.
BotFilter Pro Specializes in distinguishing between human, bot, and emulator traffic by analyzing hundreds of data points, including sensor data and network signals. Excellent at detecting automated threats; strong focus on behavioral biometrics; low false-positive rate. Primarily focused on detection and may require another service for blocking; can add latency to requests.
Install Verifier Service A post-install analysis tool that flags fraudulent installs by identifying anomalies like high new device rates and short user sessions characteristic of emulator farms. Effective for cleaning up attribution data; identifies patterns of large-scale fraud; helps in requesting refunds from ad networks. Not a real-time prevention tool; operates on historical data, meaning the ad spend has already occurred.

📊 KPI & Metrics

Tracking the right KPIs is crucial for evaluating the effectiveness of emulator detection efforts. It's important to measure not only the technical accuracy of the detection methods but also their impact on business outcomes like ad spend efficiency and customer acquisition cost. These metrics help businesses understand the ROI of their fraud prevention solutions.

Metric Name Description Business Relevance
Emulator Detection Rate The percentage of incoming fraudulent traffic correctly identified as originating from an emulator. Measures the core effectiveness of the fraud detection model in identifying specific threats.
Invalid Traffic (IVT) Rate The overall percentage of traffic flagged as invalid, including emulators, bots, and other fraudulent sources. Provides a high-level view of traffic quality and the scale of the fraud problem.
False Positive Rate The percentage of legitimate user traffic that is incorrectly flagged as fraudulent. Crucial for ensuring that fraud prevention measures do not block real potential customers.
Blocked Ad Spend The monetary value of fraudulent clicks and impressions that were successfully blocked. Directly quantifies the ROI of the fraud protection system in terms of saved advertising budget.
Clean Traffic Ratio The proportion of traffic that is verified as legitimate after filtering out all invalid sources. Helps in assessing the quality of traffic from different ad networks or campaigns.

These metrics are typically monitored through real-time dashboards provided by the traffic protection service. Alerts can be configured to notify teams of sudden spikes in fraudulent activity, such as a new emulator farm attack. The feedback from these metrics is essential for continuously tuning detection rules and optimizing the performance of fraud filters.

🆚 Comparison with Other Detection Methods

Detection Accuracy and Sophistication

Emulator detection is highly specialized and effective against fraud originating from virtualized environments. Compared to general IP blocklisting, which can be blunt and lead to high false positives, emulator detection is more precise because it analyzes device-specific signals. However, it is less effective against fraud from real device farms. Behavioral analytics offers a broader approach that can detect both emulated and human-driven fraud but may require more data to achieve high accuracy.

Processing Speed and Scalability

Signature-based emulator detection (checking for known emulator properties) is extremely fast and scalable, making it suitable for real-time, high-volume environments. It is often faster than complex behavioral analysis, which requires more computational resources to process session data. CAPTCHAs, another method, are slow and disrupt the user experience, making them unsuitable for passive fraud detection in advertising flows.

Effectiveness Against New Threats

Emulator detection that relies on fixed signatures can be evaded by new or custom-built emulators. In this regard, behavioral analytics is more adaptable, as it focuses on identifying non-human patterns regardless of the specific software used. A hybrid approach, combining signature-based checks with behavioral and anomaly detection, provides the most robust defense against both known and emerging threats from emulated devices.

⚠️ Limitations & Drawbacks

While crucial for fraud prevention, relying solely on emulator detection has its limitations. Sophisticated fraudsters constantly evolve their techniques to bypass detection, and certain legitimate scenarios can be misidentified as fraud. This makes it essential to use emulator detection as part of a multi-layered security strategy.

  • High Resource Consumption: Deep analysis of device properties and behavior for every single request can be computationally intensive, potentially adding latency and cost at scale.
  • Evasion by Sophisticated Emulators: Advanced emulators are specifically designed to mimic real devices perfectly, making them capable of passing basic detection checks by spoofing hardware IDs and sensor data.
  • False Positives: Overly strict rules can incorrectly flag legitimate users who may be using privacy tools or have unusual device configurations, leading to blocked conversions.
  • Inability to Stop Device Farm Fraud: Emulator detection is ineffective against fraud committed on thousands of real, physical devices controlled by scripts (device farms). These are not emulators and will pass device integrity checks.
  • Detection Latency: Some forms of emulator fraud, especially those involving complex in-app behavior, can only be identified through post-install analysis, meaning the initial ad spend is already lost.

In cases where fraud is highly sophisticated or originates from real devices, a hybrid approach combining emulator detection with behavioral biometrics and machine learning is more suitable.

❓ Frequently Asked Questions

How is an emulated device different from a bot on a real device?

An emulated device is entirely software-based, mimicking a physical device on a computer, often in a data center. A bot on a real device, however, is a script that automates actions on an actual smartphone, often as part of a "device farm." Emulator detection focuses on identifying the virtual environment, whereas detecting bots on real devices requires behavioral analysis.

Can emulator detection accidentally block legitimate users?

Yes, this is known as a false positive. Developers legitimately use emulators to test their applications, and some privacy-focused users might employ tools that can make their devices appear like emulators. A well-tuned fraud detection system uses multiple signals, not just one, to minimize the risk of blocking real users.

Is emulator detection enough to stop all ad fraud?

No, emulator detection is a crucial component but not a complete solution. It is ineffective against other major fraud types like click spamming, ad stacking, and fraud originating from real device farms. A comprehensive ad fraud strategy requires a multi-layered approach that includes various detection methods.

How does emulator detection handle new or unknown emulators?

While signature-based methods fail against new emulators, advanced systems use behavioral analysis and anomaly detection. They look for non-human patterns, inconsistencies between data points (like IP location vs. device language), and the absence of real sensor data, which can identify new threats without a pre-existing signature.

Does using an emulator for app testing risk being flagged as fraudulent?

It can be. Traffic from developer emulators can be flagged by fraud detection systems. To avoid this, development teams should use specific test environments, internal IP whitelisting, or test accounts that are separated from live user acquisition campaigns to prevent their testing activity from being misidentified as fraudulent.

🧾 Summary

Emulated devices are software programs that mimic real mobile devices to perpetrate ad fraud by generating fake clicks, installs, and engagement. Detecting this activity is vital for protecting advertising budgets and maintaining data accuracy. Protection systems analyze device fingerprints, behavioral patterns, and network signals to identify the non-human characteristics of emulators, blocking this fraudulent traffic before it can waste ad spend and corrupt marketing analytics.