Web Request Classification Using ML Bot Categories

The patent replaces fixed mechanical criteria-based bot identification with a machine learning engine that uses supervised learning algorithms. The machine learning engine analyzes multiple features from web requests (headers, behavior patterns, request characteristics) to categorize bots, substituting rigid rule-based systems with adaptive computational models that improve measurement precision while managing complexity through automated training processes.

The patent changes the parameters used for bot identification from simple fixed criteria to multiple dynamic features extracted from web requests. The system analyzes various parameters including HTTP headers, request patterns, timing information, and behavioral characteristics, transforming the identification process from single-parameter fixed thresholds to multi-parameter adaptive analysis, thereby improving categorization accuracy.

The patent implements preliminary action by pre-training machine learning models with historical web request data before deployment. The machine learning engine is trained in advance using supervised learning on labeled datasets containing various bot and human traffic patterns. This preliminary training enables the system to make rapid accurate classifications during actual operation without performing complex analysis in real-time, thus improving identification accuracy while maintaining processing speed.

The patent segments the bot identification process into distinct phases: feature extraction from web requests, input to machine learning engine, classification decision, and confidence level assignment. This segmentation allows the system to distribute computational complexity across multiple stages, with the machine learning engine handling the complex pattern recognition while other components manage data preparation and result interpretation, improving overall precision without excessive processing time.

The patent implements universality by designing a machine learning engine that can categorize multiple types of bots using a single unified system. The engine handles diverse bot categories including web scrapers, credential stuffers, account takeover bots, and other automated traffic through the same supervised learning framework. This multi-functional approach provides adaptability to handle different bot types while maintaining ease of operation through centralized automated classification and confidence-based decision-making.

The patent incorporates feedback mechanisms where the machine learning engine assigns confidence levels to each bot categorization decision. This confidence feedback allows the system to adapt its handling of different bot types based on the certainty of classification. High-confidence classifications can be handled automatically with established policies, while lower-confidence cases can trigger additional verification or manual review, providing operational simplicity while maintaining high adaptability to various bot scenarios.