Forensics Aware Lossless Compression of CAN Traffic Logs

  • Andras Gazdag
  • Levente Buttyan
  • Zsolt Szalay
Keywords: CAN, network traffic capture, semantic compression, forensic analysis

Abstract

In this paper, we propose a compression method that allows for the efficient storage of large amounts of CAN traffic data, which is needed for the forensic investigations of accidents caused by the cyber-attacks on vehicles. Compression of recorded CAN traffic also reduces the time (or bandwidth) needed to off-load that data from the vehicle. In addition, our compression method allows analysts to perform log analysis on the compressed data. It is shown that the proposed compression format is a powerful tool to find traces of a cyber-attack. We achieve this by performing semantic compression on the CAN traffic logs, rather than the simple syntactic compression. Our compression method is lossless, thus preserving all information for later analysis. Besides all the above advantages, the compression ratio that we achieve is better than the compression ratio of the state-of-the-art syntactic compression methods, such as zip.

Author Biographies

Andras Gazdag

Laboratory of Cryptography and System Security, Department of Networked Systems and Services, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, Hungary

Levente Buttyan

Laboratory of Cryptography and System Security, Department of Networked Systems and Services, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, Hungary

Zsolt Szalay

Department of Automotive Technologies, Faculty of Transportation Engineering and Vehicle Engineering, Budapest University of Technology and Economics, Hungary

Published
2017-12-31
How to Cite
Gazdag, A., Buttyan, L., & Szalay, Z. (2017). Forensics Aware Lossless Compression of CAN Traffic Logs. Communications - Scientific Letters of the University of Zilina, 19(4), 105-110. Retrieved from http://journals.uniza.sk/index.php/communications/article/view/278
Section
Articles