Historical Network Analysis for Complex Attack Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing network detection methods are ineffective in detecting complex attacks, particularly webshell attacks, due to their reliance on shallow signature-based approaches and inability to utilize stateful information, leading to high false positives and negatives.
Innovation Solution
A system that maintains and processes historical network activity data to identify complex attacks using machine learning models, allowing for the detection and mitigation of webshell attacks without requiring endpoint management or complete signature coverage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If shallow signature-based detection methods are used, then device complexity is reduced, but detection precision deteriorates leading to high false positives and negatives
Solution Approach 1:
The patent transitions from shallow signature-based detection to deep packet inspection that analyzes multiple dimensions of network traffic including payload content, protocol structure, behavioral patterns, and contextual information. This multi-dimensional analysis enables accurate detection of complex attacks like webshells without requiring proportionally increased system complexity
Solution Approach 2:
The patent replaces traditional mechanical signature-matching mechanisms with machine learning models that automatically learn attack patterns from historical data. These models detect anomalies and malicious behaviors without relying on pre-defined signatures, significantly improving detection precision while managing complexity through automated feature extraction
2Measurement precision
If historical network data is collected and analyzed, then detection precision improves, but loss of time increases due to data processing requirements
Solution Approach 1:
The patent implements continuous collection and preprocessing of historical network traffic data, organizing it into structured formats with extracted features ready for analysis. This preliminary preparation ensures that when attacks occur, the machine learning models can quickly query and analyze pre-processed data without time-consuming real-time processing
Solution Approach 2:
The patent creates simplified representations and features from raw network traffic data, storing these extracted characteristics rather than analyzing complete raw packets during detection. This copying approach maintains detection precision while significantly reducing the time required for real-time analysis
3Measurement precision
If stateful information is utilized for detection, then detection precision improves, but device complexity increases
Solution Approach 1:
The patent implements a unified machine learning-based detection framework that handles multiple attack types (webshells, SQL injection, XSS, etc.) and multiple data sources (network traffic, endpoint data, threat intelligence) through a single system architecture. This multi-functional approach improves detection precision for complex attacks while avoiding the complexity of separate specialized systems
Data Source
AI summary
Various embodiments provide systems and methods for identifying malicious network behavior based upon historical analysis.