Encoded Data Compression With Probability-Based Intrusion Detection
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Solution Overview
Problem
Current data storage technologies face challenges in keeping pace with rapidly increasing data demand due to limited storage capacity and bandwidth constraints, and existing intrusion detection systems are inadequate in processing encrypted packets, prone to false positives, and dependent on frequent signature library updates.
Innovation Solution
A system and method for real-time data compaction with integrated intrusion detection, which measures the probability distribution of encoded data streams, compares it to a reference distribution, and uses statistical algorithms to detect anomalies, thereby generating alerts for potential intrusions without relying on signature libraries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data compression is used to increase storage capacity, then storage efficiency improves, but intrusion detection capability deteriorates
Solution Approach 1:
The patent merges data compression and intrusion detection into a unified system where the compression algorithm's probability distribution analysis is simultaneously used for both compression and security monitoring. The compression engine and intrusion detection module share the same computational framework, allowing both functions to operate together without requiring separate systems.
Solution Approach 2:
The system implements feedback by continuously monitoring the probability distribution of compressed data and comparing it against expected distributions. When deviations are detected, the system generates alerts while maintaining normal compression operations, allowing dynamic adjustment and continuous improvement of detection accuracy without disrupting storage efficiency.
2Reliability
If traditional intrusion detection systems are used to detect attacks, then security monitoring is provided, but system complexity increases
Solution Approach 1:
The compression algorithm serves multiple functions simultaneously: it compresses data for storage efficiency, analyzes probability distributions for intrusion detection, and provides a unified framework that eliminates the need for separate detection systems. This multi-functionality reduces overall system complexity while maintaining detection capability.
Solution Approach 2:
The compression system performs intrusion detection as a byproduct of its normal operation. The probability distribution analysis required for optimal compression automatically provides the data needed for security monitoring, allowing the system to detect intrusions without adding separate detection infrastructure or increasing overall complexity.
3Measurement precision
If signature library updates are performed frequently to detect latest threats, then detection accuracy improves, but maintenance overhead increases
Solution Approach 1:
The system uses dynamic probability distribution analysis that automatically adapts to new threats without requiring manual updates. The compression algorithm continuously learns from incoming data patterns, allowing the detection system to evolve dynamically with emerging threats while maintaining accuracy without manual intervention or maintenance overhead.
Solution Approach 2:
The intrusion detection system automatically updates its detection capabilities through continuous probability distribution analysis of incoming data. The system self-adjusts to new threat patterns without requiring external signature library updates, eliminating maintenance overhead while preserving detection accuracy through autonomous adaptation.
4Speed
If data transmission bandwidth is increased to handle large data sets, then transmission speed improves, but infrastructure cost increases
Solution Approach 1:
The system changes the parameter of data representation by encoding information in compressed form with reduced bandwidth requirements. The probability distribution-based compression maintains information integrity while significantly reducing the amount of data transmitted, allowing faster effective transmission speeds over existing bandwidth infrastructure without requiring additional capacity.
Data Source
AI summary
A system and method for data compression with intrusion detection, that measures in real-time the probability distribution of an encoded data stream, compares the probability distribution to a reference probability distribution, and uses one or more statistical algorithms to determine the divergence between the two sets of probability distributions to determine if an unusual distribution is the result of a data intrusion. The system comprises both encoding and decoding machines, an intrusion detection module, a codebook training module, and various databases which perform various analyses on encoded data streams.