Network Traffic Detection Using Patricia Trees and Direct Tables
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Solution Overview
Problem
Existing network protection tools are ineffective against malicious viruses as they rely on recognizing specific patterns, which can be easily altered by hackers, failing to provide sufficient power and control to detect new or modified viruses.
Innovation Solution
A detection system utilizing a Network Processor with a Direct Table and Patricia trees to monitor network traffic, correlating TCP SYN packets with specific Source Addresses and Destination Ports to identify and report malicious packets, focusing on statistical behavior rather than known patterns, and employing algorithms for real-time detection and response.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pattern-based virus detection tools are used, then detection accuracy for known viruses is improved, but adaptability to new or modified viruses deteriorates
Solution Approach 1:
The system dynamically adapts its detection parameters and thresholds based on observed network traffic patterns. Instead of using fixed pattern-matching rules, the system continuously learns and adjusts its detection criteria to identify malicious behavior, enabling it to detect both known and new virus variants without requiring constant pattern database updates.
Solution Approach 2:
The system changes detection parameters from static pattern signatures to dynamic statistical thresholds. By monitoring traffic volume, packet rates, and behavioral patterns over time, the system adjusts its detection sensitivity and thresholds to accommodate new virus types while maintaining accuracy for known threats, thus resolving the contradiction between precision and adaptability.
2Reliability
If pattern-based detection is implemented, then detection capability for specific viruses is improved, but control power for network administrators deteriorates
Solution Approach 1:
The system implements feedback mechanisms that provide network administrators with real-time information about detected threats, traffic patterns, and system performance. Administrators can adjust detection thresholds, whitelist specific traffic patterns, and configure response actions based on observed network behavior, thereby maintaining both high detection reliability and operational control.
Solution Approach 2:
The system performs self-adjustment by automatically learning normal network patterns and identifying anomalies without requiring constant administrator intervention. This autonomous operation maintains high detection reliability while reducing the operational burden on administrators, who only need to review and approve significant findings rather than manually configure every detection parameter.
Data Source
AI summary
A detection and response system including a set of algorithms for detection within a stream of normal computer traffic a subset of TCP packets with one IP Source Address (SA), one Destination Port (DP), and a number exceeding a threshold of distinct Destination Addresses (DA). There is efficient use of a lookup mechanism such as a Direct Table and Patricia search tree to record sets of packets with one SA and one DP as well as the set of DA values observed for the given SA, DP combination. The existence of such a subset and the header values including SA, DP, and multiple DAs of the subset are reported to a network administrator. In addition, various administrative responses to reports are provided.