| Title | Long Short-Term Memory-Based Intrusion Detection System for In-Vehicle Controller Area Network Bus |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Hossain, Md Delwar, Inoue, Hiroyuki, Ochiai, Hideya, FALL, Doudou, Kadobayashi, Youki |
| Conference Name | 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC) |
| Date Published | jul |
| Keywords | automobiles, CAN, connected car security, controller area network security, Cyber-physical systems, Deep Learning, fuzzing, Hidden Markov models, Internet of Things, Intrusion detection, intrusion detection system, LSTM, machine learning, Protocols, pubcrawl, Resiliency |
| Abstract | The Controller Area Network (CAN) bus system works inside connected cars as a central system for communication between electronic control units (ECUs). Despite its central importance, the CAN does not support an authentication mechanism, i.e., CAN messages are broadcast without basic security features. As a result, it is easy for attackers to launch attacks at the CAN bus network system. Attackers can compromise the CAN bus system in several ways: denial of service, fuzzing, spoofing, etc. It is imperative to devise methodologies to protect modern cars against the aforementioned attacks. In this paper, we propose a Long Short-Term Memory (LSTM)-based Intrusion Detection System (IDS) to detect and mitigate the CAN bus network attacks. We first inject attacks at the CAN bus system in a car that we have at our disposal to generate the attack dataset, which we use to test and train our model. Our results demonstrate that our classifier is efficient in detecting the CAN attacks. We achieved a detection accuracy of 99.9949%. |
| DOI | 10.1109/COMPSAC48688.2020.00011 |
| Citation Key | hossain_long_2020 |
Long Short-Term Memory-Based Intrusion Detection System for In-Vehicle Controller Area Network Bus