Browser Bot Detection via ML Feature Vectors

The patent replaces traditional mechanical rule-based detection systems with machine learning models that automatically learn patterns from request data. The system uses trained models to classify browsers as legitimate or malicious, substituting manual rule creation with automated intelligent analysis that achieves higher detection accuracy.

The machine learning models are trained on historical request data and continuously improve their detection capabilities autonomously. The system self-adjusts by learning from new data patterns, reducing the need for manual intervention and rule updates while maintaining high detection accuracy.

The system performs model training in advance using historical data, so that when actual detection is needed, the pre-trained models can quickly classify new requests. This preliminary action separates the computationally intensive training phase from the lighter inference phase, reducing real-time resource consumption.

The system extracts and uses only the most relevant features from request data for classification, rather than processing all possible data points. This partial action approach focuses computational resources on the most discriminative features, reducing overall resource consumption while maintaining detection accuracy.

The system segments the request analysis process into distinct feature extraction and classification stages. By dividing the comprehensive analysis into modular components, the system can efficiently process requests through specialized sub-routines, maintaining both thorough analysis and processing speed.

The machine learning models transform raw request parameters into optimized feature representations that are more efficient to process. By changing the parameter space from raw data to learned features, the system achieves reliable detection with reduced computational overhead and faster processing.