Compressed Data Stream Intrusion Detection Using Multi-Scale Entropy
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Solution Overview
Problem
Current data storage and transmission systems are overwhelmed by exponential data growth, and existing intrusion detection systems are inadequate against classical and quantum computing threats, particularly due to reliance on signature libraries and vulnerability to quantum-generated patterns.
Innovation Solution
A data compression system with real-time multi-scale entropy analysis and quantum-resistant intrusion detection capabilities, identifying anomalies by measuring deviation from expected probability distributions and incorporating adaptive training to detect both classical and quantum-generated threats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If signature-based intrusion detection systems are used, then known attacks can be detected, but the system cannot detect quantum-generated patterns and requires frequent updates
Solution Approach 1:
The patent replaces the mechanical signature-matching system with a statistical entropy analysis system. Instead of comparing data against known attack signatures, the system calculates entropy metrics of incoming data streams and compares them against baseline entropy profiles to detect anomalies, including quantum-generated patterns, without requiring signature updates
Solution Approach 2:
The system changes the detection parameter from signature matching to entropy measurement. By monitoring statistical properties such as Shannon entropy, block entropy, and spectral entropy of data streams, the system can identify quantum attacks based on their distinctive statistical characteristics rather than requiring knowledge of specific attack signatures
2Quantity of substance
If data compression is applied to reduce storage demand, then storage capacity is improved, but transmission bandwidth becomes a bottleneck
Solution Approach 1:
The patent implements a universal data representation system where data is encoded into a standardized format that simultaneously achieves compression for storage efficiency and maintains transmission efficiency. The system uses entropy-based encoding that adapts to different data types while maintaining optimal compression ratios and transmission speeds
3Quantity of substance
If physical storage capacity is increased to meet data growth, then storage demand is satisfied, but manufacturing capacity constraints prevent solving the problem
Solution Approach 1:
The system changes the fundamental parameter of data representation to achieve higher density storage. By encoding data in an optimized format that exploits statistical properties and redundancies, the system effectively increases storage capacity through mathematical transformation rather than physical expansion
Data Source
AI summary
Data compression with quantum-resistant intrusion detection, that measures in real-time the probability distribution of an encoded data stream and analyzes entropy characteristics across multiple bit-scale windows to detect both classical and quantum-generated intrusions. The system compares the probability distribution to a reference probability distribution and uses statistical algorithms to determine divergence between distributions while simultaneously analyzing entropy cascade patterns characteristic of quantum computing sources. When divergence exceeds configured thresholds or quantum-generated characteristics are detected, the system generates intrusion alerts identifying the threat type. The system comprises encoding and decoding machines, an intrusion detection engine that performs multi-scale entropy analysis, a codebook training engine that creates quantum-resistant codebooks using entropy-stratified training algorithms, and databases including a quantum signature database storing compression patterns of known quantum algorithms. The codebook training engine adaptively retrains encoding algorithms upon detecting new quantum patterns, maintaining system effectiveness against evolving quantum threats.