Attack-Aware Capability Assessment Method Based on Specific Semantic Search
Daojuan Zhang, Qi Zhao, Yuman Wang, Shu Li, Zheng Tian, Yizhen Sun · 2024
In this paper, we propose an attack perception capability assessment method based on the specific semantic search for the problem of attack perception capability assessment of security protection equipment for power monitoring systems. The method aims to improve the recognition and localization accuracy of common attacks by accurately correlating the output products of security equipment with the execution of attack test cases. The research employs an attack-specific semantic generation technique that combines attack attribute values with timestamps to form specific semantics for subsequent matching. Pattern matching algorithm, specifically the BM (Boyer-Moore) algorithm, is further utilized to match the specific semantics with the output products of the security device Snort to achieve attack localization. Experimental results demonstrate that Snort exhibits high accuracy in perceiving the majority of attack types and exhibits an enhanced perception rate compared to certain baseline methods. In summary, the method proposed in this paper effectively improves the sensing ability of security equipment to attacks on the power monitoring system and provides a new idea for the performance evaluation of security protection equipment.