Web Application Anomaly Detection via Log Clustering

The patent segments the large volume of web application logs and database logs into smaller, manageable clusters using machine learning algorithms. This segmentation allows the system to process and analyze log data in organized groups rather than as a single overwhelming dataset, significantly improving detection speed while maintaining accuracy.

The patent introduces machine learning algorithms as an intermediary between the raw log data and human investigators. These algorithms automatically perform initial analysis, clustering, and anomaly detection, filtering out normal traffic patterns and highlighting only suspicious activities that require human review, thereby reducing the time investigators need to spend on manual analysis.

The patent implements a multi-functional machine learning system that performs multiple detection tasks simultaneously - clustering logs, identifying anomalies, comparing against baseline patterns, and generating alerts. This universal approach consolidates what would otherwise require multiple separate tools and processes into a single integrated system, improving efficiency without proportionally increasing complexity.

The machine learning system is designed to be self-training and self-improving. It automatically learns from new data patterns, updates its baseline clusters, and refines its detection algorithms without requiring constant manual reconfiguration. This self-service capability reduces the operational complexity of maintaining the system over time.

The patent applies partial automation by using machine learning to handle the majority of log analysis tasks automatically, while reserving manual review only for the small subset of cases that the algorithm identifies as potentially suspicious. This partial action approach maintains high detection accuracy through human oversight of critical cases while dramatically reducing administrative overhead by eliminating manual review of normal traffic.

The system dynamically adjusts detection parameters and thresholds based on learned patterns from the data. By changing parameters adaptively rather than using fixed thresholds, the system maintains high reliability in detecting actual threats while automatically filtering out false positives, thereby reducing the administrative overhead associated with manual verification.