Research on Anomaly Detection in Vehicular CAN Based on Bi-LSTM

Authors

  • Xiaopeng Kan School of Cyber Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
  • Zhihong Zhou 1) Institute of Cyber Science and Technology, Shanghai Jiao Tong University, Shanghai, China 2) Shanghai Key Laboratory of Integrated Administration Technologies for Information Security, Shanghai, China
  • Lihong Yao School of Cyber Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
  • Yuxin Zuo School of Cyber Science and Engineering, Shanghai Jiao Tong University, Shanghai, China

DOI:

https://doi.org/10.13052/jcsm2245-1439.1251

Keywords:

Internet of vehicles, CAN, Anomaly Detection, Bi-LSTM

Abstract

Controller Area Network (CAN) is one of the most widely used in-vehicle networks in modern vehicles. Due to the lack of security mechanisms such as encryption and authentication, CAN is vulnerable to external hackers in the intelligent network environment. In the paper, a lightweight CAN bus anomaly detection model based on the Bi-LSTM model is proposed. The Bi-LSTM model learns ID sequence correlation features to detect anomalies. At the same time, the Attention mechanism is introduced to improve the model’s efficiency. The paper focuses on replay attacks, denial of service attacks and fuzzing attacks. The experimental results show that the anomaly detection model based on Bi-LSTM can detect three attack types quickly and accurately.

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Author Biographies

Xiaopeng Kan, School of Cyber Science and Engineering, Shanghai Jiao Tong University, Shanghai, China

Xiaoping Kan is currently studying for a master’s degree at the School of Cyberspace Security, Shanghai Jiao Tong University, China. His main research areas are the security of Internet of Vehicles.

Zhihong Zhou, 1) Institute of Cyber Science and Technology, Shanghai Jiao Tong University, Shanghai, China 2) Shanghai Key Laboratory of Integrated Administration Technologies for Information Security, Shanghai, China

Zhihong Zhou received the Ph.D degree in Electronic engineering from Zhejiang University in 2005. He is currently working as an assistant researcher at the Institute of Cyber Science and Technology of Shanghai Jiao Tong University. His research areas include Network Security Risk Assessment, Cryptographic Application Security Assessment etc.

Lihong Yao, School of Cyber Science and Engineering, Shanghai Jiao Tong University, Shanghai, China

Lihong Yao received the Ph.D degree in Computer Science from Nanjing University in 2003. Now, she is an associate professor at the School of Cyberspace Security, Shanghai Jiao Tong University, China. Her research interests mainly include security of Internet of Vehicles and Big Data Analysis.

Yuxin Zuo, School of Cyber Science and Engineering, Shanghai Jiao Tong University, Shanghai, China

Yuxin Zuo is currently studying for a bachelor’s degree at the School of Cyberspace Security, Shanghai Jiao Tong University, China. His main research interests are the security of Internet of Things and Big Data Analysis.

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Published

2023-08-12

How to Cite

1.
Kan X, Zhou Z, Yao L, Zuo Y. Research on Anomaly Detection in Vehicular CAN Based on Bi-LSTM. JCSANDM [Internet]. 2023 Aug. 12 [cited 2024 Apr. 28];12(05):629-52. Available from: https://journals.riverpublishers.com/index.php/JCSANDM/article/view/21515

Issue

2023: Vol 12 Iss 5

Section

Cyber Security Issues and Solutions

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