공격 횟수와 공격 유형을 고려하여 탐지 성능을 개선한차량 내 네트워크의 침입 탐지 시스템
Intrusion Detection System for In-Vehicle Network to Improve Detection Performance Considering Attack Counts and Attack Types
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초록
본 논문에서는 공격 횟수와 공격 유형을 모두 고려하여 차량 내 네트워크에서 해킹을 탐지하는 침입 탐지 시스템의 성능을 개선하는 기법을 제안한다. 침입 탐지 시스템에서 침입을 정상으로 잘못 인식하는 FNR(False Negative Rate)과 정상을 침입으로 잘못인식하는 FPR(False Positive Rate)은 모두 차량의 안전에 큰 영향을 미친다. 본 논문에서는 일정 홧수 이상 공격으로 탐지된 데이터 프레임을 자동적으로 공격으로 처리하며, 자동 공격으로 판단하는 방법도 공격 유형에 따라 다르게 적용함으로서 FNR과 FPR을모두 개선하는 침입 탐지 기법을 제안하였다. 시뮬레이션 결과 제안하는 기법은 DoS(Denial of Service) 공격과 Spoofing 공격에서 FNR과 FPR을 효과적으로 개선할 수 있었다.
Abstract
This paper proposes an intrusion detection system for in-vehicle network to improve detection performanceconsidering attack counts and attack types. In intrusion detection system, both FNR (False Negative Rate), whereintrusion frame is misjudged as normal frame, and FPR (False Positive Rate), where normal frame is misjudged asintrusion frame, seriously affect vechicle safety. This paper proposes a novel intrusion detection algorithm toimprove both FNR and FPR, where data frame previously detected as intrusion above certain attack counts isautomatically detected as intrusion and the automatic intrusion detection method is adaptively applied accordingto attack types. From the simulation results, the propsoed method effectively improve both FNR and FPR in DoS(Denial of Service) attack and spoofing attack.
참고문헌 (12)
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