Journal of IKEEE (전기전자학회논문지)

Institute of Korean Electrical and Electronics Engineers (한국전기전자학회)
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Data Preprocessing Method for Lightweight Automotive Intrusion Detection System

차량용 경량화 침입 탐지 시스템을 위한 데이터 전처리 기법

  • Sangmin Park (School of Electronic Engineering and Department of Intelligent Semiconductor, Soongsil University) ;
  • Hyungchul Im (School of Electronic Engineering and Department of Intelligent Semiconductor, Soongsil University) ;
  • Seongsoo Lee (School of Electronic Engineering and Department of Intelligent Semiconductor, Soongsil University)
  • Received : 2023.12.14
  • Accepted : 2023.12.21
  • Published : 2023.12.31
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Abstract

This paper proposes a sliding window method with frame feature insertion for immediate attack detection on in-vehicle networks. This method guarantees real-time attack detection by labeling based on the attack status of the current frame. Experiments show that the proposed method improves detection performance by giving more weight to the current frame in CNN computation. The proposed model was designed based on a lightweight LeNet-5 architecture and it achieves 100% detection for DoS attacks. Additionally, by comparing the complexity with conventional models, the proposed model has been proven to be more suitable for resource-constrained devices like ECUs.

본 논문에서는 차량 내 네트워크에서 즉각적인 공격 탐지를 위해 프레임 피처 삽입이 적용된 슬라이딩 윈도우 기법을 제안한다. 이 방법은 현재 프레임의 공격 여부에 따라 라벨링을 진행하기 때문에 공격 탐지의 실시간성을 보장할 수 있다. 또한 이 방법이 CNN 연산에서 현재 프레임에 대한 가중치를 주어 성능을 향상시킬 수 있음을 실험을 통해 확인하였다. 제안하는 모델은 경량화된 LeNet-5 구조 기반으로 설계되었으며 DoS 공격 탐지 성능에서 100%를 달성하였다. 또한 기존 연구의 모델들과 복잡성을 비교했을 때 제안하는 모델이 ECU와 같이 리소스가 제한된 장치에 더 적합함을 확인하였다.

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). (20023805, RS-2022-00155731, RS-2022-00232192)

References

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