Feature-Weighted Clustering for Unstructured Traffic Sessions
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Solution Overview
Problem
Discerning traffic patterns in unstructured network traffic is challenging for cybersecurity appliances, as existing methods struggle to identify applications based on unstructured protocol data units (PDUs) without standardized formats.
Innovation Solution
A cybersecurity appliance employs a feature weighted distance engine to cluster unstructured TCP traffic using importance weights for feature vectors, generating high-quality clusters and accurate patterns through a natural language processor, tokenizer, and clustering model, enabling identification of malicious sessions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing methods are used to analyze unstructured traffic, then device complexity is reduced, but measurement precision and reliability of traffic pattern identification deteriorate
Solution Approach 1:
The patent segments the traffic analysis process into distinct components: feature extraction module, clustering module, and pattern identification module. Each module handles specific aspects of unstructured traffic analysis, improving overall precision while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The patent introduces feature vectors as intermediary representations that bridge raw unstructured traffic data and high-level pattern identification. These feature vectors capture essential characteristics while abstracting away unnecessary complexity, enabling accurate pattern recognition without requiring direct analysis of complex raw traffic structures.
2Productivity
If feature weighted distance engine is used for clustering, then productivity and measurement precision improve, but device complexity increases
Solution Approach 1:
The patent transforms traffic characteristics into numerical feature vectors with associated weights, converting complex unstructured data into a format suitable for efficient mathematical clustering operations. This parameter transformation enables high-speed computation while capturing the essential structure of unstructured traffic patterns.
Solution Approach 2:
The patent replaces manual traffic analysis methods with automated computational clustering algorithms that operate on feature vectors. This substitution of mechanical/algorithmic processes enables high-speed, high-precision clustering that would be impractical through traditional analysis methods.
3Measurement precision
If natural language processor and tokenizer are employed, then measurement precision of traffic pattern identification improves, but use of energy increases
Solution Approach 1:
The patent performs preliminary tokenization and feature extraction on traffic data before final pattern identification. This preliminary processing breaks down complex traffic structures into manageable units, reducing the computational energy required for subsequent analysis while maintaining high identification accuracy.
Solution Approach 2:
The patent extracts and isolates specific meaningful features from unstructured traffic data using tokenization techniques. By separating and analyzing only the relevant features rather than processing entire traffic streams, the system achieves high measurement precision with reduced energy consumption.
Data Source
AI summary
A natural language processor extracts features from batches of unstructured traffic. A feature weighted distance engine computes a distance matrix between pairs of feature vectors for sessions of unstructured traffic using a weight vector that assigns importance to relative placement of features in feature vectors. The distance function used to compute the distance matrix with the weight vector is conducive to generating high-quality clusters and patterns in unstructured traffic. The sessions of unstructured traffic are clustered according to the pairwise distance matrix. Generated clusters are merged with clusters for previously analyzed sessions of unstructured traffic. A pattern identification engine extracts patterns from the merged clusters that correspond to behavior of applications generating the unstructured traffic.