Stream Fuzzy Hash Algorithm for Real-Time Network Traffic Analysis
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Solution Overview
Problem
Traditional fuzzy hash algorithms are inadequate for processing streaming network traffic due to issues with out-of-order data, missing initial length, incomplete files, and inefficient processing of streaming data, which limits their applicability in real-time network traffic analysis.
Innovation Solution
The Stream Fuzzy Hash (SFH) algorithm processes streaming data by using a sliding window for weak hash calculation, Galois Field matrix multiplication for strong hashing, and interval indexing to handle unordered and incomplete data, allowing for real-time summary generation and detection of network traffic.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional fuzzy hash algorithm is used for offline data processing, then file similarity identification accuracy is improved, but processing efficiency for streaming data deteriorates due to multiple file reads and constant adjustment of slicing conditions
Solution Approach 1:
The patent segments the file processing into fixed-size blocks and processes each block independently through the hash function. This allows streaming data to be processed in chunks without requiring multiple reads of the entire file, thereby improving processing efficiency while maintaining similarity identification accuracy through consistent block-wise hashing
Solution Approach 2:
The patent pre-calculates and stores the hash values of all data blocks before final similarity comparison. This preliminary hashing action eliminates the need for constant adjustment of slicing conditions during processing, allowing streaming data to be processed efficiently in a single pass while maintaining accurate similarity measurement
2Reliability
If data is cached to solve out-of-order arrival problem, then data completeness is improved, but memory overhead increases
Solution Approach 1:
The patent segments incoming streaming data into fixed-size blocks and processes each block independently as it arrives. This segmentation allows the system to handle out-of-order data blocks without caching entire files, reducing memory overhead while maintaining data completeness through block-level processing and incremental hash accumulation
Solution Approach 2:
The patent introduces an intermediary blocking mechanism that temporarily holds individual data blocks in a buffer rather than caching complete files. This intermediary approach allows out-of-order blocks to be processed and reassembled incrementally, ensuring data completeness while minimizing memory usage by only storing partial block data rather than entire files
3Manufacturing precision
If multiple reads and constant adjustment of slicing conditions are performed, then hash value length appropriateness is improved, but processing time increases for streaming data
Solution Approach 1:
The patent dynamically adjusts the slicing conditions and block processing parameters during the initial phase of streaming data reception, then stabilizes these parameters for subsequent processing. This dynamic adjustment approach allows the system to adapt to varying data characteristics initially, then maintain consistent hash value length appropriateness without repeated adjustments, reducing processing time while preserving precision
Solution Approach 2:
The patent changes key processing parameters such as block size and slicing thresholds during the initialization phase based on initial data characteristics, then maintains these optimized parameters for the remainder of the streaming process. This parameter change strategy ensures appropriate hash value length is achieved early on, eliminating the need for constant parameter adjustments and reducing overall processing time
Data Source
AI summary
The present invention provides a method, a system, and a device for a hash generation and network traffic detection. It uses a method of storing intermediate calculation results to perform hash calculation for streaming data, and uses a matrix multiplication operation as a strong hash algorithm to reduce memory occupation. The present invention can generate hash in real time in the case of streaming data comprising defects, unordered, and overlapping, which is suitable for detecting files from network traffic, and is applicable to virus detection, intrusion detection, data anti-leakage, network content review, digital forensics, digital rights protection, and other fields.