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LSTM-Based Intrusion Detection System for In-Vehicle Can Bus Communications

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dc.contributor.author Hossain, Md Delwar
dc.contributor.author Doudou, Fall
dc.contributor.author Kadobayashi, Youki
dc.date.accessioned 2021-03-05T08:43:37Z
dc.date.available 2021-03-05T08:43:37Z
dc.date.issued 2020-10-07
dc.identifier.uri http://hdl.handle.net/10061/14221
dc.description.abstract The modern automobile is a complex piece of technology that uses the Controller Area Network (CAN) bus system as a central system for managing the communication between the electronic control units (ECUs). Despite its central importance, the CAN bus system does not support authentication and authorization mechanisms, 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 including Denial of Service (DoS), Fuzzing and Spoofing attacks. 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 generate our own dataset by first extracting attack-free data from our experimental car and by injecting attacks into the latter and collecting the dataset. We use the dataset for testing and training our model. With our selected hyper-parameter values, our results demonstrate that our classifier is efficient in detecting the CAN bus network attacks, we achieved an overall detection accuracy of 99.995%. We also compare the proposed LSTM method with the Survival Analysis for automobile IDS dataset which is developed by the Hacking and Countermeasure Research Lab, Korea. Our proposed LSTM model achieves a higher detection rate than the Survival Analysis method. ja_JP
dc.language.iso en ja_JP
dc.publisher IEEE ja_JP
dc.relation.isreplacedby https://ieeexplore.ieee.org/document/9216166 ja_JP
dc.rights IEEE is not the copyright holder of this material. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ ja_JP
dc.subject Automobiles ja_JP
dc.subject Fuzzing ja_JP
dc.subject Protocols ja_JP
dc.subject Intrusion detection ja_JP
dc.subject Machine learning ja_JP
dc.subject Computer crime ja_JP
dc.title LSTM-Based Intrusion Detection System for In-Vehicle Can Bus Communications ja_JP
dc.type.nii Journal Article ja_JP
dc.contributor.transcription カドバヤシ, ユウキ
dc.contributor.alternative 門林
dc.textversion none ja_JP
dc.identifier.eissn 2169-3536
dc.identifier.jtitle IEEE Access ja_JP
dc.identifier.volume 8 ja_JP
dc.identifier.spage 185489 ja_JP
dc.identifier.epage 185502 ja_JP
dc.relation.doi 10.1109/ACCESS.2020.3029307 ja_JP
dc.identifier.NAIST-ID 86635919 ja_JP
dc.identifier.NAIST-ID 82049578 ja_JP
dc.identifier.NAIST-ID 73292476 ja_JP


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