Network Security Workbench for Multi-Stage Threat Identification
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Solution Overview
Problem
Traditional threat identification methods in network security are inadequate in handling complex and evolving threat attack methods, leading to reduced accuracy and inability to cope with diverse and changing threat events.
Innovation Solution
A network security operation workbench comprising a data monitoring module, threat analysis module, risk processing module, and log generation module, which performs real-time monitoring, secondary identification of abnormal data, and generates threat management logs to improve recognition accuracy and security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional manually defined rules are used for threat identification, then identification accuracy of known threats is high, but the system becomes unable to cope with complex and changeable threat attack methods
Solution Approach 1:
The threat identification process is segmented into multiple stages: initial rule-based filtering for known threats, followed by machine learning-based analysis for unknown or complex threats. This segmentation allows each method to operate in its optimal domain, maintaining high accuracy for known threats while improving adaptability to new threat types.
Solution Approach 2:
The system combines multiple identification approaches (rule-based methods and machine learning algorithms) into a composite identification mechanism. This composite approach leverages the strengths of both methods: the precision of rule-based systems and the adaptability of machine learning, thereby resolving the contradiction between accuracy and versatility.
2Measurement precision
If more identification algorithms are used to improve threat recognition accuracy, then recognition accuracy improves, but system complexity increases
Solution Approach 1:
The system dynamically selects and applies identification algorithms based on the characteristics of the threat data being analyzed. Rather than using all algorithms simultaneously, the system adapts its identification approach in real-time, applying simple rule-based methods when applicable and more complex machine learning algorithms only when necessary, thereby maintaining accuracy while managing complexity.
Solution Approach 2:
The machine learning models are trained automatically on historical threat data and continuously improve their own performance without requiring manual intervention for each new threat type. This self-service capability allows the system to maintain high recognition accuracy while reducing the operational complexity of managing multiple algorithms.
Data Source
AI summary
A network security operation workbench is provided, and relates to the technical field of network security, and includes: a data monitoring module, configured for performing security monitoring on network events in systems, identifying abnormal data and outputting to obtain abnormal information; a threat analysis module, configured for performing secondary identification on the abnormal information by using preset identification algorithms, obtaining threat types and levels corresponding to abnormal events in the abnormal information, and generating threat identification information; a risk processing module, configured for analyzing the threat identification information, matching to obtain corresponding coping strategies and methods, and performing security management operations on the abnormal events based on the coping strategies and methods; a log generation module, configured for recording an identification and analysis process and a security management operation process of each of abnormal events in the systems and generating a threat management log.