Journal of IKEEE (전기전자학회논문지)

Institute of Korean Electrical and Electronics Engineers (한국전기전자학회)
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Ultra-Light-Weight Automotive Intrusion Detection System Using Random Sample Consensus

랜덤 샘플 합의를 사용한 초경량 차량용 침입 탐지 시스템

  • Jonggwon Kim (School of Electronic Engineering and Department of Intelligent Semiconductor, Soongsil University) ;
  • Hyungchul Im (School of Electronic Engineering and Department of Intelligent Semiconductor, Soongsil University) ;
  • Joosock Lee (School of Electronic Engineering and Department of Intelligent Semiconductor, Soongsil University) ;
  • Seongsoo Lee (School of Electronic Engineering and Department of Intelligent Semiconductor, Soongsil University)
  • 김종권 ;
  • 임형철 ;
  • 이주석 ;
  • 이성수
  • Received : 2024.09.23
  • Accepted : 2024.09.25
  • Published : 2024.09.30
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Abstract

This paper proposes an effective method for detecting hacking attacks in automotive CAN bus using the RANSAC (Random Sample Consensus) algorithm. Conventional deep learning-based detection techniques are difficult to be applied to resource-constrained environments such as vehicles. In this paper, the attack detection performance in vehicular CAN communication has been improved by utilizing the lightweight nature and efficiency of the RANSAC algorithm. The RANSAC algorithm can perform effective detection with minimal computational resources, providing a practical hacking detection solution for vehicles.

본 논문은 RANSAC(Random Sample Consensus) 알고리즘을 활용하여 차량용 CAN 통신에서 발생하는 해킹 공격을 효과적으로 탐지하는 방법을 제안한다. 기존에 제안된 딥러닝 기반 탐지 기법은 차량과 같이 리소스가 제한된 환경에는 적용하기 어렵다는 한계가 있다. 본 논문에서는 RANSAC 알고리즘의 경량성과 효율성을 활용하여 차량용 CAN 통신에서의 공격 탐지 성능을 향상시켰다. RANSAC 알고리즘은 적은 연산 자원으로도 효과적인 탐지를 수행할 수 있어서 차량에 탑재 가능한 실용적인 해킹 탐지 솔루션을 제공할 수 있다.

Keywords

Acknowledgement

This work was supported by the R&D Program of the Ministry of Trade, Industry, and Energy (MOTIE) and Korea Evaluation Institute of Industrial Technology (KEIT). (RS-2022-00154973, RS-2023-00232192, RS-2024-00403397). It was also supported by MOTIE and Korea Institute for Advancement of Technology (KIAT) (P0012451). The authors wish to thank IC Design Education Center (IDEC) for CAD support.

References

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