Vehicular Intrusion Detection System for Controller Area Network: A Comprehensive Survey and Evaluation
May 22, 2025 ยท The Cartographer ยท ๐ IEEE transactions on intelligent transportation systems (Print)
"No code URL or promise found in abstract"
"Title-pattern auto-detect: Vehicular Intrusion Detection System for Controller Area Network: A Comprehensive Survey and Evaluat"
Evidence collected by the PWNC Scanner
Authors
Yangyang Liu, Lei Xue, Sishan Wang, Xiapu Luo, Kaifa Zhao, Pengfei Jing, Xiaobo Ma, Yajuan Tang, Haiying Zhou
arXiv ID
2505.17274
Category
cs.CR: Cryptography & Security
Citations
5
Venue
IEEE transactions on intelligent transportation systems (Print)
Last Checked
3 days ago
Abstract
The progress of automotive technologies has made cybersecurity a crucial focus, leading to various cyber attacks. These attacks primarily target the Controller Area Network (CAN) and specialized Electronic Control Units (ECUs). In order to mitigate these attacks and bolster the security of vehicular systems, numerous defense solutions have been proposed.These solutions aim to detect diverse forms of vehicular attacks. However, the practical implementation of these solutions still presents certain limitations and challenges. In light of these circumstances, this paper undertakes a thorough examination of existing vehicular attacks and defense strategies employed against the CAN and ECUs. The objective is to provide valuable insights and inform the future design of Vehicular Intrusion Detection Systems (VIDS). The findings of our investigation reveal that the examined VIDS primarily concentrate on particular categories of attacks, neglecting the broader spectrum of potential threats. Moreover, we provide a comprehensive overview of the significant challenges encountered in implementing a robust and feasible VIDS. Additionally, we put forth several defense recommendations based on our study findings, aiming to inform and guide the future design of VIDS in the context of vehicular security.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
๐ Similar Papers
In the same crypt โ Cryptography & Security
R.I.P.
๐ป
Ghosted
R.I.P.
๐ป
Ghosted
The Limitations of Deep Learning in Adversarial Settings
R.I.P.
๐ป
Ghosted
Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks
R.I.P.
๐ป
Ghosted
Spectre Attacks: Exploiting Speculative Execution
R.I.P.
๐ป
Ghosted
How To Backdoor Federated Learning
R.I.P.
๐ป
Ghosted