System and method for network weight compression and intrusion detection

Abstract

A system and method for neural network weight compression with intrusion detection capabilities that optimizes model storage and transmission while providing security. The system analyzes weight characteristics to identify statistical properties within different neural network layers, generates optimized encoding schemes based on the analysis, and creates reference distributions for security verification. The compression process employs a multi-resolution approach that produces a progressive representation with base and enhancement layers, enabling flexible deployment across diverse computing environments. Security markers and statistical fingerprints can be embedded throughout the encoded representation, allowing for detection of unauthorized modifications during transmission or deployment. The system monitors encoded weight streams, measures distribution divergence against reference baselines, and generates alerts when statistical anomalies indicate potential tampering. This approach achieves superior compression ratios while maintaining model performance and providing robust protection against increasingly sophisticated attacks targeting neural network weights.