What is Device Fingerprinting? How Does It Fight Fraud?

Much like the transformative shift advertising experienced from offline to online, the field of cybersecurity witnessed a game-changer with the advent of device fingerprinting. You might be wondering What is device fingerprinting?

This innovative approach offers a unique opportunity for enhanced security and individualized insights. As online businesses seek a more reliable method beyond the declining efficacy of web cookies, device fingerprinting emerges as a strong solution to fortify digital defenses and gain deeper, more personalized understandings of user interactions.

Read this article to learn more about what is device fingerprinting and how it is used to prevent fraud.

What is Device Fingerprinting?

Device fingerprinting is a method used to collect information about a device for the purpose of identification. It involves gathering various attributes and characteristics of a device, such as its operating system, browser version, screen resolution, plugins, language settings, and other hardware or software-related details.

A digital fingerprint is created by analyzing this unique combination of attributes. This, in turn, can be used to identify and distinguish one device from another.

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How Does Device Fingerprinting Work?

To answer the question of how device fingerprinting works, we need to know that device fingerprinting collects a set of attributes and characteristics from a device. This collection creates a unique identifier or fingerprint for that specific device. Here's a general overview of how device fingerprinting works:

1. Data Collection

  • Browser and User Agent Information: Details about the user's browser, including the user-agent string, browser version, and installed plugins.
  • Operating System: Information about the device's operating system, including the version and platform.
  • Hardware Information: Details about the device's hardware, such as screen resolution, device orientation, and available fonts.
  • Network Information: IP address, network-related details, and geolocation information.
  • Cookies and Local Storage: Information stored in cookies or local storage can be used to identify returning users.

2. JavaScript and Client Side Techniques

  • Canvas Fingerprinting: Rendering a hidden image or text on a user's device and analyzing the unique characteristics of how the device renders it, creating a fingerprint.
  • WebGL Fingerprinting: Utilizing the device's WebGL capabilities to create a unique identifier based on its graphics rendering.
  • Browser API Usage: Gathering information from various browser APIs, such as the Battery API, to add additional data points to the fingerprint.

3. Behavioral Analysis

  • Mouse Movements and Click Patterns: Analyzing the way a user interacts with a webpage by tracking mouse movements, clicks, and other behavioral patterns.
  • Keystroke Dynamics: Monitoring typing patterns and keystrokes to add another layer of uniqueness to the fingerprint.

4. Server Side Processing

  • Hashing and Aggregation: The collected data is often hashed or processed in a way that combines multiple attributes into a single identifier, making it more resistant to reverse engineering.
  • Comparison and Matching: The generated fingerprint is compared to a database of known fingerprints to identify or authenticate the device.

5. Updating Fingerprint

  • Dynamic Attributes: Some fingerprinting methods take into account dynamic attributes, such as current time or user interactions, to create a fingerprint that can change over time.
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How Do Cookie Fingerprints Differ from Device Fingerprints?

Cookie fingerprints primarily rely on data stored in browser cookies, serving as a unique identifier that aids in detecting multi-accounting attempts and other suspicious activities upon website entry. Unlike device fingerprints, which encompass a broader range of attributes, cookie data is stored locally on users' devices.

While device fingerprints analyze various aspects such as browser version, installed plugins, and screen resolution to create a unique identifier for each device, cookie fingerprints are based solely on information stored by the browser. However, the efficacy of cookie fingerprinting is limited by users' ability to control their cookie settings, allowing potential fraudsters to easily manipulate or delete their cookie sessions to avoid detection.

Conversely, device fingerprinting collects and stores information about users' hardware, software, and browser configurations on a server-side database, making the data more accessible to merchants and less susceptible to manipulation from the user side.

While users with identical device models and settings may share similar hardware configurations, device fingerprinting, when coupled with additional data points, remains a valuable tool for identifying potential fraudulent activities.

6 Uses for Device Fingerprinting

These are just a few uses with device fingerprint examples, and the applications of device fingerprinting can vary across industries and use cases. The overarching theme is that device fingerprinting enables more personalized, secure, and efficient interactions in digital environments. Along with the following uses, we provide you with device fingerprint examples.

1. Authentication

Device fingerprinting is used for secure authentication processes. Systems can verify the device’s identity during login attempts by recognizing the unique attributes of a device.

For example, two-factor authentication systems may use device fingerprinting to ensure that the device trying to access an account is recognized and trusted.

2. Customization

Device fingerprints aid in tailoring content and services based on the preferences and behavior of a specific device.

For example, streaming platforms use fingerprint devices to recommend content based on viewing history and preferences associated with a particular device.

3. Antibot Measures

Device fingerprinting helps in identifying and preventing automated bot activities on websites, reducing fraudulent interactions.

For example, captcha mechanisms can use fingerprint devices to differentiate between genuine user interactions and automated bot attempts.

4. Digital Rights Management

Device fingerprints play a role in ensuring compliance with content usage policies and protecting digital assets from unauthorized distribution.

For example, streaming services may use device fingerprinting to enforce restrictions on the number of devices that can access premium content.

5. Device Management

Device fingerprinting assists in tracking and managing devices within networks, providing valuable insights for IT purposes.

For example, IT administrators use device fingerprinting to monitor the types of devices connected to a corporate network and enforce security policies accordingly.

6. User Recognition

Device fingerprinting enhances user experience by recognizing returning visitors and adapting features based on their historical interactions.

For example, ecommerce sites can use device fingerprints to personalize product recommendations and promotions for users with a known browsing history.

How do companies benefit from using device fingerprinting?

Companies use device fingerprinting for various reasons, leveraging its capabilities to enhance security, improve user experience, and streamline operations. Here are some key reasons why companies use device fingerprinting:

  1. User Authentication and Security: Companies employ device fingerprinting as an additional layer of authentication to enhance security.
  1. Fraud Prevention and Detection: Device fingerprinting helps identify and prevent fraudulent activities by recognizing unusual or suspicious device behavior.
  1. Personalization and User Experience: Device fingerprints enable companies to customize user experiences based on device preferences and historical behavior.
  1. Analytics and Marketing: Device fingerprinting aids in tracking user behavior for analytics and targeted marketing efforts.
  1. Content Protection: Companies use device fingerprinting to protect digital content from unauthorized access and distribution.
  1. AntiBot Measures: Device fingerprinting helps distinguish between genuine user interactions and automated bot activities, reducing the impact of malicious bot traffic.
  1. CrossDevice Linking: Device fingerprints assist in linking user activities across different devices for a seamless and consistent user experience.
  1. Compliance and Security Policies: Device fingerprinting supports companies in adhering to regulatory requirements and implementing specific security policies based on device attributes.

Device Fingerprinting and Fraud Prevention

Device fingerprinting provides a unique identifier for individual devices. This helps in distinguishing legitimate users from potential fraudsters. Here's how device fingerprinting contributes to fraud prevention:

1. Unique Identification

Device fingerprinting collects various attributes and characteristics of a device, such as hardware details, software configurations, browser settings, and more. These attributes, when combined, create a unique fingerprint for each device. This unique identifier helps in distinguishing between legitimate users and potential fraudsters.

2. Behavioral Analysis

Device fingerprinting not only captures static device information but can also analyze dynamic factors like user behavior, login patterns, and transaction history. By analyzing these patterns, it can identify anomalies or suspicious activities that may indicate fraudulent behavior.

3. Multifactor Authentication

Device fingerprinting can be integrated into multifactor authentication systems. When combined with other authentication factors like passwords, biometrics, or one-time codes, device fingerprinting adds an additional layer of security, making it harder for fraudsters to gain unauthorized access.

4. Continuous Authentication

Device fingerprinting can contribute to continuous authentication by constantly monitoring and validating the legitimacy of a user throughout their session. If there are sudden changes in the device attributes or behavior, it can trigger additional security measures or prompt the user for reauthentication.

5. Fraud Detection and Prevention

Device fingerprints are useful in building models for fraud detection. Machine learning algorithms can be trained to recognize patterns associated with known fraudulent activities based on device fingerprints, allowing for real-time detection and prevention of fraudulent transactions.

6. Cross-Device Tracking

Device fingerprinting helps in tracking user activities across different devices. This can be valuable in identifying suspicious behavior, such as accessing an account from a new device that has never been associated with the user before.

7. Reducing False Positives

By relying on a combination of device fingerprints and other authentication methods, organizations can reduce the chances of false positives (legitimate users being flagged as potential fraudsters) compared to relying solely on traditional methods.

8. Adaptive Security Measures

Device fingerprinting allows organizations to implement adaptive security measures. Based on the risk level associated with a particular device or user, security protocols can be adjusted dynamically to provide an appropriate level of protection.

Device Fingerprinting Examples: Preventing Online Fraud

Below are device fingerprinting examples and strategic applications in fraud mitigation across online platforms.

1. Identifying Bonus and Promo Abuse

An online gaming platform offers a sign-up bonus for new customers. A fraudster attempts to create multiple accounts to exploit the bonus offer.

In this case, device fingerprinting detects similar devices and password patterns. The system identifies multiple accounts originating from the same device or users attempting to spoof their data. The platform can flag and investigate suspicious accounts, preventing bonus abuse.

2. Preventing Multi-Accounting Instances

A subscription-based service wants to prevent users from sharing accounts. To stop users from sharing accounts, they use device fingerprinting to assign unique IDs to each user.

When the system detects multiple users trying to access the platform from the same device, the service can set restrictions or ask for extra authentication to prevent unauthorized sharing.

3. Mitigating Chargebacks and Friendly Fraud

Online retailers often face chargebacks and friendly fraud as customers dispute valid transactions, causing financial losses. To tackle this issue, the retailer employs device fingerprinting along with digital profiling and IP analysis to confirm customer identities.

The system can recognize patterns linked to fraudulent chargebacks by scrutinizing historical data. The system evaluates the risk level of transactions and intensifies scrutiny of suspicious cases, thereby lowering the chances of friendly fraud.

4. Enhancing Bot Attack Detection

A website containing valuable content faces bot attacks that artificially increase traffic, as bots try to imitate human behavior to bypass standard security measures. Device fingerprinting scrutinizes installed plugins, browser versions, window size, and other characteristics to combat this.

It identifies anomalies like the use of emulators or virtual machines by fraudsters. In response, the system can employ bot management techniques, either blocking or challenging suspicious traffic, effectively lessening the impact of bot attacks on the website.

5. Securing Against Unauthorized Account Access

An ecommerce platform is determined to protect user accounts from unauthorized access, especially when fraudsters try to log in using stolen credentials or unfamiliar devices.

To address this concern, device fingerprinting identifies the usual devices and browsers linked to a user's account. In cases of suspicious login attempts from unknown devices, the system triggers alerts. As a precautionary measure, the system can prompt additional verification steps or temporarily block access until the user's identity is thoroughly confirmed, ensuring heightened security for the e-commerce platform.

How Can FOCAL Help with Device Fingerprinting?

Our fraud SDKs enable you to collect vital device data directly from your customers, enhancing your ability to identify and prevent fraudulent devices effectively. This proactive approach not only strengthens security measures but also reinforces customer protection, fostering trust and confidence in your platform. By leveraging the insights gleaned from this data, you can stay ahead of potential threats, ensuring a safe and secure environment for both your business and your customers.


In conclusion, device fingerprinting proves itself as a reliable defender against online threats, ensuring businesses stay secure and gain a deeper understanding of user behavior in the ever-evolving digital landscape.


Q1. How Accurate Is Device Fingerprinting?

Device fingerprinting is highly accurate, relying on a combination of static and dynamic attributes to create a unique identifier for each device.

Q2. What Information Is Collected To Create A Device Fingerprint?

Device fingerprinting collects a range of information, including browser details, hardware configurations, IP addresses, and behavioral patterns, to create a unique fingerprint for each device.

Q3. What Are The Different Kinds Of Device Fingerprinting?

There are various types of device fingerprinting, including browser fingerprinting, canvas fingerprinting, and behavioral fingerprinting, each focusing on different aspects of device attributes.

Q4. What Is Mobile Device Fingerprint?

Mobile device fingerprinting is similar to traditional device fingerprinting but tailored specifically to mobile devices like smartphones and tablets. It serves as a tool for identifying and authenticating mobile devices, helping to prevent fraud, enhance security, and personalize user experiences in the mobile ecosystem.

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