가우시안 커널 밀도 추정 함수를 이용한 오토인코더 기반 차량용 침입 탐지 시스템
Autoencoder-Based Automotive Intrusion Detection System Using Gaussian Kernel Density Estimation Function
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초록
본 논문에서는 비지도학습 모델인 오토인코더와 가우시안 커널 밀도 추정 함수를 이용하여 차량용 CAN 네트워크에서 비정상적인 데이터를 탐지하는 방안을 제안한다. 제안하는 오토인코더 모델은 정상 데이터에서 CAN 프레임의 ID만으로 학습시킨다. 이후가우시안 커널 밀도 추정 함수를 이용하여 구한 최적의 프레임 개수와 손실 임계값을 가지는 모델을 사용하여 비정상 데이터를 효과적으로 탐지한다. DoS 공격, Gear 스푸핑 공격, RPM 스푸핑 공격, Fuzzy 공격 등 4가지 공격 데이터로 오토인코더 기반 IDS를검증하였으며 성능을 평가하였다. 기존 비지도학습 기반 모델들과 비교했을 때 우수한 성능을 나타냈으며 모든 평가 지표에서 99% 이상의 성능을 나타냈다.
Abstract
This paper proposes an approach to detect abnormal data in automotive controller area network (CAN) using an unsupervised learning model, i.e. autoencoder and Gaussian kernel density estimation function. The proposed autoencoder model is trained with only message ID of CAN data frames. Afterwards, by employing the Gaussian kernel density estimation function, it effectively detects abnormal data based on the trained model characterized by the optimally determined number of frames and a loss threshold. It was verified and evaluated using four types of attack data, i.e. DoS attacks, gear spoofing attacks, RPM spoofing attacks, and fuzzy attacks. Compared with conventional unsupervised learning-based models, it has achieved over 99% detection performance across all evaluation metrics
참고문헌 (20)
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