In our previous blogs, we discussed the overview of machine learning approaches used in Radware Bot Manager and delved deep into various approaches. This blog introduces the Semi-Supervised Behavioral Analysis Module, a deep learning-based system designed to detect bots by combining supervised and unsupervised machine learning techniques. This modular architecture enables precise identification of anomalous and bot-like behaviors in real-time traffic. Here’s a technical breakdown of its components and functionality.
Architecture and Workflow
1. Data Preprocessing and Segmentation
The module processes visitor data collected through source identifiers and evaluates requests based on features such as:
- Requested URLs
- Referrers
- Cookie counters
- Timestamps
URLs and referrers are hashed into fixed-length vectors, and delta values of cookies and timestamps capture behavioral patterns. Visitor requests are segmented into smaller chunks (e.g., sequences of 20 requests). Each segment is transformed into a multidimensional matrix for analysis.
2. Supervised Encoder Network
At the core of this module is a Long Short-Term Memory (LSTM)-based Recurrent Neural Network (RNN).
- Training: The network is trained on historically labeled data to differentiate between human and bot behaviors.
- Feature Extraction: Once trained, the network's encoder portion generates fixed-length feature vectors from visitor data. These vectors capture behavioral patterns for use in unsupervised analyses.
3. Unsupervised Anomaly Detection
The anomaly detection subsystem identifies irregular visitor behaviors using algorithms like Isolation Forest.
- Training: Based on historical data, the subsystem learns to recognize standard behavioral patterns.
- Detection: It computes anomaly scores for live traffic, flagging visitors who deviate significantly from normal behaviors.
4. Unsupervised Cluster Detection
Clustering algorithms (e.g., DBSCAN) analyze the feature vectors to detect groups of visitors with similar behavior patterns.
- This approach is particularly useful for identifying botnets, which often exhibit coordinated activity.
- Cluster information is processed to determine which groups represent malicious activity.
5. One-Class Collective Bot Intelligence Learner
This component enhances detection by identifying patterns common to bots across multiple web properties.
- An auto-encoder network compresses and decompresses visitor data to measure reconstruction errors.
- Bots typically exhibit low reconstruction error due to shared patterns. This allows the module to detect advanced or mutated bots that might not align with previously observed behaviors.
6. Adaptive Learner for Action
The adaptive learner processes inputs from anomaly detection, cluster detection, and the bot intelligence learner to take appropriate actions.
- Actions include blocking access and presenting CAPTCHAs.
- Feedback mechanisms (e.g., CAPTCHA responses) refine the module's thresholds and improve accuracy over time.
Training and Prediction Pipeline
- Training Phase: Models are trained offline using comprehensive historical datasets. This ensures adaptability across diverse traffic patterns and web properties.
- Prediction Phase: At runtime, the trained models generate fast, low-latency predictions.
- Actions are performed at a granular level, such as targeting specific user agents or tracking cookies, to minimize false positives.
Modular Components in Action
The Semi-Supervised Behavioral Analysis Module integrates its various systems for cohesive bot detection:
- Data Encoding: Real-time visitor data is encoded into fixed-length feature vectors by the supervised encoder.
- Behavioral Insights: Anomaly detection and cluster detection identify deviations and patterns indicative of malicious activity.
- Collective Intelligence: The auto-encoder detects advanced bots by comparing new patterns against previously identified ones.
- Response Mechanisms: The adaptive learner ensures appropriate and timely action against suspicious visitors while continuously improving detection capabilities.
By leveraging the strengths of both, supervised and unsupervised learning, the Semi-Supervised Behavioral Analysis Module offers a structured, data-driven approach to bot detection. Its modular architecture ensures adaptability to diverse and evolving traffic patterns, enabling precise behavioral analysis at scale. This advanced system provides a robust foundation for identifying and mitigating bot threats effectively, all while optimizing real-time decision-making processes for a secure and seamless user experience.