VBSF: Vulnerability Behavior Scanning Framework for Intelligent Autonomous Transport Systems

Hao Ren, Tao Zheng, Lei Zhang, Wenxian Wang, Meng Li, Hongwei Li

Abstract: Vulnerability behavior scanning plays a crucial role in securing Intelligent Autonomous Transportation Systems by ensuring protected communications and maintaining data integrity. Current scanning solutions, however, demonstrate several critical shortcomings: (1) their dependence on static analysis methods with predetermined scanning locations prevents dynamic adjustment of scanning strategies; (2) their limited capacity to capture data across multiple system layers fails to address sophisticated multi-layered attack patterns; and (3) their inability to dynamically activate monitoring probes hinders timely responses to newly emerging threats. To resolve these limitations, we present $\textsf {VBSF}$ , an efficient and non-intrusive vulnerability scanning framework built upon extended Berkeley Packet Filter technology. The proposed system incorporates two key innovations: a dynamic probe activation mechanism that intelligently adjusts scanning locations in real-time to optimize resource usage, and a standardized data format that enables integrated analysis of vulnerability behaviors across different system layers. Experimental evaluations confirm that $\textsf {VBSF}$ effectively identifies critical vulnerability behaviors in diverse attack scenarios while introducing only 1.47% additional system overhead.

External IDs:dblp:journals/tits/RenZZWLL25