Network Intrusion Detection via Semantic Clustering Taxonomy
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Solution Overview
Problem
Current network security methods face challenges in accurately identifying potential threats due to dynamic attack methods and high volumes of network events, leading to false positives and resource wastage.
Innovation Solution
A system utilizing a network scanner with taxonomies to detect possible intrusions, an intrusion detector to identify actual threats, and a false-positive/true-positive detector to update the taxonomy, improving threat assessment accuracy by refining semantic clustering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pattern analysis is employed to monitor network traffic for intrusion detection, then potential security threats can be identified, but false positives increase and system reliability degrades
Solution Approach 1:
The patent segments the intrusion detection process into multiple specialized components: network traffic monitors divide traffic into flows, semantic analyzers process different aspects separately, and threat databases are organized into hierarchical taxonomies. This segmentation allows each component to specialize, improving overall detection accuracy while reducing false positives through coordinated analysis.
Solution Approach 2:
The patent introduces semantic analysis as an intermediary layer between raw network traffic monitoring and threat database comparison. This intermediary processes and contextualizes traffic patterns before matching them against known threats, enabling more accurate distinction between legitimate and malicious activity, thereby reducing false positives while maintaining reliability.
2Device complexity
If the monitoring space is simplified to reduce complexity, then system operation is easier to manage, but patterns trigger false positives and management resources are wasted
Solution Approach 1:
The monitoring system is segmented into specialized modules (traffic monitors, semantic analyzers, threat databases) that each handle specific aspects of analysis. This modular approach manages complexity through clear division of labor while maintaining high detection accuracy, preventing resource waste from false positives.
Solution Approach 2:
Different parts of the monitoring system apply different levels and types of analysis appropriate to their function. Network traffic monitors handle high-volume packet inspection, semantic analyzers apply contextual understanding, and threat databases provide specialized pattern matching. This local optimization of analysis quality reduces overall resource consumption while maintaining effectiveness.
3Adaptability or versatility
If pattern analysis is continuously updated to match evolving attack methods, then detection capability is maintained, but the difficulty of detecting and measuring threats increases
Solution Approach 1:
The system employs dynamic taxonomies and threat databases that are continuously updated with new attack patterns. The semantic analysis components adapt to evolving threats by learning from new data, allowing the system to maintain high detection capability against changing attack methods while managing complexity through structured adaptation.
Solution Approach 2:
The system incorporates feedback mechanisms where detection results, including false positives and missed threats, are used to refine and update the threat databases and semantic analysis rules. This continuous feedback loop improves detection accuracy over time while systematically managing the complexity of adapting to new threats.
Data Source
AI summary
An apparatus, a method, and a computer program are provided for distinguishing relevant security threats. With conventional computer systems, distinguishing security threats from actual security threats is a complex and difficult task because of the general inability to quantify a “threat.” By the use of an intelligent conceptual clustering technique, threats can be accurately distinguished from benign behaviors. Thus, electronic commerce, and Information Technology systems generally, can be made safer without sacrificing efficiency.