Automatic Worm Signature Generation via Regular Expression Analysis
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Solution Overview
Problem
Current methods for mitigating worm attacks in computer networks are inadequate, as signature-based systems require human intervention and are ineffective in early stages, while anomaly-based systems allow some attack traffic to propagate before containment, and neither provides comprehensive solutions for unknown Internet worms.
Innovation Solution
A system and method using a novel algorithm for automatic generation of worm signatures based on regular expressions, combining anomaly detection and signature-based detection to rapidly identify and block malicious traffic, capable of generating signatures within seconds of initial detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If signature-based detection is used, then detection accuracy for known worms is improved, but response time deteriorates due to human intervention requirements
Solution Approach 1:
The system enables automatic signature generation through self-service mechanisms. When anomalous traffic is detected, the system automatically collects traffic sequences, generates signatures using algorithms like regular expressions or decision trees, and updates the signature database without human intervention, thereby maintaining high detection accuracy while dramatically reducing response time
Solution Approach 2:
The system performs preliminary actions by proactively generating signatures as soon as anomalous traffic patterns are detected. Instead of waiting for human analysis, the system pre-generates signatures and updates the database in advance, ensuring that detection accuracy is maintained while response time is minimized through automated preliminary signature creation
2Loss of time
If anomaly-based detection is used, then response time is improved, but detection accuracy deteriorates as some attack traffic propagates before blocking
Solution Approach 1:
The system implements feedback mechanisms where anomaly detection results are continuously fed into the signature generation process. When anomalies are detected, the system collects traffic sequences, generates signatures, and updates the database, creating a closed-loop feedback system that improves detection accuracy over time while maintaining rapid response through automated signature creation and deployment
Solution Approach 2:
The system introduces an intermediary signature generation module that bridges anomaly-based detection and signature-based prevention. This intermediary automatically transforms detected anomalies into actionable signatures, enabling the system to respond rapidly to anomalies while ensuring high detection accuracy through automated signature creation that captures the essence of the detected attack patterns
3Measurement precision
If manual signature creation is used, then signature precision is improved, but automation level deteriorates
Solution Approach 1:
The system employs self-service algorithms including regular expressions, decision trees, and machine learning models that automatically analyze traffic sequences and generate precise signatures without human intervention. These algorithms serve themselves by autonomously identifying patterns, creating signatures, and updating the database, thereby maintaining high signature precision while achieving full automation
Solution Approach 2:
The system replaces the mechanical process of manual signature creation with automated computational algorithms. Instead of relying on human experts to manually craft signatures, the system uses algorithmic approaches like regular expressions, decision trees, and machine learning to automatically generate precise signatures, substituting human mechanical work with automated intelligent systems that maintain or improve signature precision
4Productivity
If rapid signature generation is implemented, then productivity is improved, but system complexity deteriorates
Solution Approach 1:
The system segments the signature generation process into distinct modular components: anomaly detection module, traffic sequence collection module, signature generation module (with multiple algorithm options), and database update module. This segmentation enables rapid parallel processing of different functions while managing complexity through clear separation of concerns, allowing the system to generate signatures quickly through coordinated modular operations
Solution Approach 2:
The system implements dynamic algorithm selection that adapts to different attack scenarios. The signature generation module can dynamically switch between different algorithms (regular expressions, decision trees, machine learning) based on the characteristics of the detected anomaly, enabling rapid response to diverse threats while managing complexity through adaptive rather than static architecture
Data Source
AI summary
A computer-implemented method for mitigating attacks of malicious traffic in a computer network includes receiving a set of attack sequences, including first traffic sequences suspected of containing the malicious traffic, analyzing the attack sequences so as to automatically extract a regular expression that matches at least a portion of the attack sequences in the set, and comparing second traffic sequences to the regular expression in order to identify the second traffic sequences that contain the malicious traffic.