Worm Detection via Traffic Behavior Tree Analysis
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Solution Overview
Problem
Current systems are ineffective in automatically detecting new computer worms in networks, leading to delayed detection and widespread infection, as they rely on manual identification of known characteristics and struggle to predict new exploitation methods.
Innovation Solution
Monitoring network traffic to identify specific traffic behaviors and organizing them into a tree-like data structure to detect worm propagation patterns, such as depth, branching factor, and propagation time, allowing for the automatic identification of potential worm-infected hosts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual detection methods are used to identify known worms, then detection accuracy for known worms is improved, but detection speed for new worms deteriorates and automation level remains low
Solution Approach 1:
The system automatically detects worms by analyzing traffic behavior patterns and organizing them into tree structures without human intervention. The detection system serves itself by autonomously identifying propagation patterns, calculating metrics like branching factors and propagation times, and generating alerts, eliminating the need for manual worm identification while maintaining high detection accuracy for both known and new worms.
Solution Approach 2:
The patent replaces manual mechanical detection processes with automated computational analysis. Instead of human analysts manually examining traffic patterns, the system uses computer-based algorithms to automatically organize traffic behaviors into tree structures, calculate propagation metrics, and identify worm infections, dramatically increasing detection speed while maintaining precision.
2Reliability
If manual identification of worm characteristics is used, then detection reliability for known worms is improved, but response time to new worms deteriorates
Solution Approach 1:
The system performs preliminary analysis by continuously monitoring and organizing traffic behaviors into tree structures in advance. When a worm infection occurs, the propagation patterns are already captured in the tree data structure, allowing immediate detection and response. The system proactively builds the infrastructure for detection before worms appear, enabling rapid response while maintaining reliability through consistent pattern analysis.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring network traffic, analyzing propagation patterns, and adjusting detection parameters based on observed behaviors. The tree structure provides feedback on infection spread, allowing the system to refine its detection accuracy over time and maintain high reliability while responding rapidly to new worm variants.
3Productivity
If automated detection systems are implemented, then detection speed and response time are improved, but system complexity increases
Solution Approach 1:
The patent segments the complex detection task into manageable components: traffic behavior identification, tree structure organization, metric calculation (branching factors, propagation times), and pattern recognition. Each component handles a specific aspect of worm detection, making the overall system more manageable and maintainable while achieving high detection speed through specialized processing for each segment.
Solution Approach 2:
The tree-based data structure serves multiple functions simultaneously: it organizes traffic behaviors, tracks propagation paths, calculates infection metrics, and enables pattern recognition for different worm types. This multi-functional approach reduces system complexity by using a single unified structure rather than separate specialized systems for each detection task.
4Adaptability or versatility
If comprehensive traffic monitoring is performed to detect all worm behaviors, then detection coverage is improved, but network overhead and processing load increase
Solution Approach 1:
The system extracts only the essential and distinctive features of worm traffic behaviors rather than monitoring all network traffic in detail. By identifying and focusing on key propagation patterns such as rapid branching factors and short propagation times, the system achieves comprehensive detection coverage while minimizing network overhead by processing only the most informative traffic characteristics.
Solution Approach 2:
The system applies partial monitoring by focusing on specific traffic behaviors that are most indicative of worm propagation rather than analyzing every packet. By concentrating computational resources on detecting the critical propagation patterns that define worm infections, the system achieves effective detection coverage with reduced processing load and network overhead compared to comprehensive full-traffic analysis.
Data Source
AI summary
A worm is a malicious process that autonomously spreads itself from one host to another. To infect a host, a worm must somehow copy itself to the host. The method in which a worm transmits a copy of itself produces network traffic patterns that can be generalized as a traffic behavior. As a worm spreads itself across the network, the propagation of the traffic behavior can be witnessed as hosts are infected, one after another. By monitoring the network traffic for propagations of traffic behaviors, a presence of a worm can be detected.