Edge Network Security via Packet Transformation

The patent combines signature-based detection and anomaly-based detection into a unified intrusion detection system. The signature module handles known attacks with high precision, while the anomaly module detects unknown attacks by comparing behavior against established norms. This merging allows the system to maintain high detection precision for known threats while simultaneously gaining adaptability to detect novel, unknown attacks that deviate from normal behavior patterns.

The patent introduces a behavior baseline as an intermediary reference for anomaly detection. Instead of directly comparing suspicious activities against rigid rules, the system establishes normal behavior patterns through machine learning and uses this baseline as a mediator to evaluate deviations. This intermediary layer reduces false positives by providing a more nuanced understanding of what constitutes abnormal behavior, allowing legitimate variations in system behavior to be distinguished from actual threats.

The patent replaces traditional mechanical intrusion detection mechanisms with machine learning-based automated analysis. Instead of relying on manually configured signature rules and static security policies, the system employs machine learning algorithms that automatically adapt to new threats and patterns. This substitution maintains comprehensive security coverage while significantly improving detection efficiency by automating the analysis of complex multi-application traffic and reducing the computational burden of manual rule management.