File System Attack Detection via Statistical Process Control

The patent replaces status-based periodic monitoring with a statistical process control system that continuously analyzes file access patterns in real-time. The system uses statistical metrics (mean, standard deviation) to dynamically detect anomalies as they occur, substituting the mechanical periodic check approach with a continuous statistical analysis mechanism that provides both reliability and real-time detection capability.

The patent changes the detection parameters from signature matching (comparing file hashes against known malware signatures) to statistical parameter analysis (monitoring access frequency, timing patterns, and file type distributions). This parameter transformation reduces computational complexity while maintaining detection reliability, as statistical patterns can be calculated more efficiently than signature comparisons and are less prone to false positives from novel malware variants.

The patent creates a universal detection system that monitors file access patterns regardless of the attacker type (malware, human actors, or bots). By focusing on behavioral statistics (access frequency, timing, file types) rather than malware-specific signatures, the system achieves multi-functionality that detects diverse attack vectors through a single unified approach, enhancing adaptability while maintaining reliability.

The patent implements a feedback mechanism where the statistical process control system continuously monitors file access patterns, compares them against baseline statistics, and immediately generates alerts when anomalies are detected. This closed-loop feedback system eliminates notification delays by providing real-time feedback on security events, allowing rapid response while maintaining continuous reliable monitoring.