Data compression with signature-based verifiable intrusion detection and prediction

Abstract

A system and method for intrusion detection with prediction and validation subsystems powered by machine learning. The system analyzes real-time codeword streams and historical data to predict potential intrusions before they fully manifest. It employs various machine learning models to extract features, identify patterns, and validate detected anomalies. The system continuously learns from validated events and false positives, improving its accuracy over time. An integrated encryption module secures sensitive data using a dyadic distribution-based algorithm, combining compression and encryption. This approach significantly reduces false positives, enhances threat detection capabilities, and provides robust data protection for cybersecurity applications.

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