A TWO-STAGE DEEP LEARNING APPROACH FOR CAN INTRUSION DETECTION

With recent advancements in the automotive world and the introductions of autonomous vehicles, automotive cybersecurity has become a main and primary issue for every automaker. In order to come up with measures to detect and protect against malicious attacks, intrusion detection systems (IDS) are commonly used. These systems identify attacks while comparing normal behavior with abnormalities. In this paper, we propose a novel, two-stage IDS based on deep-learning and rule-based systems. The objective of this IDS is to detect malicious attacks and ensure CAN security in real time. Deep Learning has already been used in CAN IDS and is already proven to be a successful algorithm when it comes to extensive datasets but comes with the cost of high computational requirements. The novelty of this paper is to use Deep Learning to achieve high predictability results while keeping low computational requirements by offsetting it with rule-based systems. In addition, we examine the performance of proposed IDS with the objective for using it in real-time situations.