Analysis of Security Vulnerability Levels of In-Vehicle Network Topologies Applying Graph Representations

Abstract

This article investigates cybersecurity issues related to in-vehicle communication networks. In-vehicle communication network security is evaluated based on the protection characteristics of the network components and the topology of the network. The automotive communication network topologies are represented as undirected weighted graphs, and their vulnerability is estimated based on the specific characteristics of the generated graph. Thirteen different vehicle models have been investigated to compare the vulnerability levels of the in-vehicle network using the Dijkstra's shortest route algorithm. An important advantage of the proposed method is that it is in accordance with the most relevant security evaluation models. On the other hand, the newly introduced approach considers the Secure-by-Design concept principles.

Automotive Ethernet architecture and security: challenges and technologies

“…2. The IVN fields and their subjects in the reviewed works Field Paper(s) Subjects Requirements of automotive TSN [6], [11], [12] List of requirements for future IVN processing platforms [13], [14] Integrate TSN with SDN with security [15] NDN [16] Graceful degradation for real-time autonomous vehicle applications [17] Improve the accuracy of the canvas [10] Reducing latency by porting it to Xronos [18] TSN-5G heterogeneous networks [19] HPVUs security [20] OTT Ethernet algorithm [21] Ethernet backbone with Zone based architecture [22] iDriving [45], [46] TSN PSFP, Protection of the TSN automotive Ethernet Security protocols for IVN [23], [24], [30], [35], [39] Review of blockchain and security attributes of IVN [34] IPSEC [36] Extensions to the SOME/IP protocol for securing SOME/IP service discovery [37] CANFD [25]- [29], [33], [38] Confidentiality, authentication, integrity [30]- [33] Blockchain in IVN [40] Structure-aware CAN Fuzzing procedure [41] Mitigations against an underactuated USV Intrusions detection systems for IVN [29], [42], [44], [47], [48], [53] Intrusion Detection Systems [49], …”

“…In the performance evaluation, Gatekeeper is found to have a 0.03 ms latency overhead when transmitting CAN data and outperforms Tesla when transmitting LiDAR data, demonstrating its effectiveness and efficiency. The cybersecurity issues of in-vehicle communication networks built on the security attributes of the components and the topology of the network that were evaluated in [24]. The undirected weighted graphs were used to represent the in-vehicle communication network topologies, the vulnerabilities were assessed based on the accurate attributes of the generated graph.…”