Hierarchical Feature Extraction for Malicious Traffic Classification
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Solution Overview
Problem
Existing methods for analyzing network traffic to detect and classify threats face challenges in finding suitable feature extraction algorithms that effectively capture contextual information from proxy logs, often limiting their descriptive power or relying on a small number of features that fail to represent unusual cases or large amounts of legitimate background traffic.
Innovation Solution
A hierarchical feature extraction architecture is introduced, using a set of generic feature extraction functions and statistics across multiple levels to systematically build features that represent traffic behavior, capturing contextual information and distinguishing between malicious and legitimate traffic.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a small number of features are used for threat detection, then the analysis process is simpler and faster, but the descriptive power is limited and unusual cases cannot be effectively captured
Solution Approach 1:
The feature extraction process is segmented into multiple hierarchical levels, where each level extracts specific types of features from packet flow data. Level 1 extracts basic flow features, Level 2 extracts contextual features, and Level 3 extracts high-level behavioral features. This segmentation allows the system to process a large number of features in an organized manner, maintaining both comprehensiveness and processing efficiency.
Solution Approach 2:
The patent transforms the feature extraction problem from a single-dimensional approach to a multi-dimensional hierarchical structure. By organizing features across three levels with increasing abstraction, the system effectively increases the dimensionality of the analysis, enabling comprehensive representation of traffic behavior without overwhelming computational complexity at any single level.
2Measurement precision
If generic feature extraction functions are used across multiple levels, then contextual information is captured effectively, but the computational complexity increases
Solution Approach 1:
The computational complexity is managed by segmenting the feature extraction into three distinct levels, each handling specific types of computations. Level 1 performs basic statistical computations on packet flows, Level 2 computes contextual features from Level 1 results, and Level 3 derives high-level behavioral patterns. This segmentation distributes computational load and allows for optimized processing at each level.
Solution Approach 2:
The hierarchical structure performs preliminary feature extraction at lower levels before proceeding to higher-level analysis. By pre-computing basic flow features at Level 1 and contextual features at Level 2, the system prepares processed data that simplifies the final classification task at Level 3, reducing the overall computational burden compared to performing all analyses simultaneously.
3Ease of manufacture
If traditional feature extraction methods are used, then the implementation is simpler, but the ability to differentiate malicious traffic from legitimate background traffic is reduced
Solution Approach 1:
The patent segments the feature extraction into hierarchical levels that progressively build more sophisticated representations of traffic behavior. This segmentation maintains implementation feasibility by breaking down a complex problem into manageable stages, while simultaneously improving reliability through the cumulative extraction of increasingly discriminative features across levels.
Solution Approach 2:
By transitioning from traditional single-level feature extraction to a multi-level hierarchical approach, the patent adds dimensional depth to the feature representation. This dimensional expansion enables the system to capture subtle patterns in malicious traffic that distinguish it from legitimate background traffic, significantly improving differentiation accuracy while maintaining a structured implementation framework.
Data Source
AI summary
In one embodiment, a method includes receiving packet flow data at a feature extraction hierarchy comprising a plurality of levels, each of the levels comprising a set of feature extraction functions, computing a first set of feature vectors for the packet flow data at a first level of the feature extraction hierarchy, inputting the first set of feature vectors from the first level of the feature extraction hierarchy into a second level of the feature extraction hierarchy to compute a second set of feature vectors, and transmitting a final feature vector to a classifier to identify malicious traffic. An apparatus and logic are also disclosed herein.