In our previous blogs, we discussed the overview of machine learning approaches used in Radware Bot Manager and delved into an anomaly detection-based technique for enhancing bot detection. This blog introduces another anomaly detection approach: the Browser Anomaly Detection Module, which focuses on identifying spoofed browsers and devices using machine learning.
Overview of the Browser Anomaly Detection Module
The Browser Anomaly Detection Module employs unsupervised machine learning to identify inconsistencies in JavaScript profiles. These profiles, which encapsulate various parameters unique to browser and device configurations, are analyzed to detect deviations indicative of spoofing attempts. This approach enables detection of bots attempting to masquerade as legitimate users, often with the intent of evading traditional security mechanisms.
How It Works
The module analyzes JavaScript profiles composed of multiple parameters that reflect browser and device characteristics. These parameters include browser-specific features (e.g., webkitGetUserMedia availability) and device-dependent attributes (e.g., CPU class or heap size limits). Together, they form a comprehensive fingerprint that can be used to identify discrepancies.
To detect anomalies, the module utilizes isolation forest algorithms, a form of unsupervised machine learning. Here’s an outline of the process:
- Training the Model: The module builds separate anomaly detection models for each major user agent. Using legitimate visitor profiles as a baseline, the isolation forest recursively partitions the data into smaller subsets. Profiles that can be isolated with minimal splits are flagged as anomalies.
- Real-Time Scoring: Incoming visitor profiles are processed through the trained models. An anomaly score is generated based on how closely the visitor's JavaScript profile aligns with the expected patterns. Profiles with high anomaly scores are flagged for further action.
- Threshold Determination: Adaptive mechanisms determine a threshold score for classification, ensuring the module adapts to evolving attack patterns while minimizing false positives.
Example Anomalies
Spoofing attempts are often revealed through mismatches between JavaScript profile data and the information presented in user agent strings. For example:
- Operating System Mismatches: A user agent might indicate a Windows OS, but the JavaScript profile detects a Linux platform.
- Browser Version Conflicts: The user agent might claim to be Chrome, but the detected layout engine suggests a different browser entirely.
The Browser Anomaly Detection Module captures such inconsistencies, enabling swift and precise identification of potential bot activity.
Seamless Integration and Real-Time Actions
The module is designed to integrate seamlessly with existing infrastructure. Once trained, it processes incoming data in real time, extracting JavaScript profile features, scoring them for anomalies, and triggering actions such as CAPTCHA challenges for suspicious visitors. This streamlined pipeline ensures continuous protection against spoofing attempts.
A Complementary Layer of Defense
The Browser Anomaly Detection Module is one component of Radware’s multi-layered approach to bot detection. By analyzing JavaScript profiles for anomalies, it enhances the ability to identify sophisticated bots that mimic human behavior. Combined with other AI-driven defenses, this module plays a critical role in securing applications and safeguarding user data.
For more insights into Radware’s AI-powered security solutions, explore our blog archives or reach out to learn how we can help protect your organization from the ever-evolving threat landscape.