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Handling Raw High-Dimensional CAN Bus Data using Long Short-Term Memory Networks for Intrusion Detection in In-Vehicle Networks

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dc.contributor.author Kibrom Desta, Araya en
dc.contributor.author Ohira, Shuji en
dc.contributor.author Arai, Ismail en
dc.contributor.author Fujikawa, Kazutoshi en
dc.date.accessioned 2021-02-17T05:03:26Z en
dc.date.available 2021-02-17T05:03:26Z en
dc.date.issued 2020-11-27 en
dc.identifier.isbn 978-1-7281-8827-0 en
dc.identifier.uri http://hdl.handle.net/10061/14217 en
dc.description 2020 30th International Telecommunication Networks and Applications Conference (ITNAC) en
dc.description.abstract CAN uses no authentication and encryption mechanisms for secure communication. To solve the security issues of the CAN bus, a deep learning-based intrusion detection systems have been proposed. But due to the high dimensional property of the CAN bus data, it was not possible to create an effective Intrusion Detection System (IDS) in the CAN bus that can take the property of the CAN data into consideration. In this paper, we are proposing a Long Short-Term Memory Networks (LSTM) based IDS that can handle the high dimensional property of the CAN bus data . Unlike the conventional methods which required a single network architecture for each unique arbitration ID, our method gives a single overall anomaly signal over a certain detection window without the need for reverese-engineering the CAN bus data. Using this anomaly signal we have managed to achieve 100% detection precision for insertion, fuzzy and targeted attacks in our data and in a public data that is prepared for this specific purpose. en
dc.language.iso en en
dc.rights ©2020 IEEE ja
dc.rights 出版社許諾条件により、本文は2022年11月28日以降に公開 ja
dc.title Handling Raw High-Dimensional CAN Bus Data using Long Short-Term Memory Networks for Intrusion Detection in In-Vehicle Networks en
dc.type.nii Conference Paper en
dc.contributor.transcription オオヒラ, シュウジ ja
dc.contributor.transcription フジカワ, カズトシ ja
dc.contributor.alternative 大平, 修慈 ja
dc.contributor.alternative 新井, イスマイル ja
dc.contributor.alternative 藤川, 和利 ja
dc.textversion author en
dc.identifier.volume 1 en
dc.identifier.spage 1 en
dc.identifier.epage 7 en
dc.relation.doi 10.1109/ITNAC50341.2020.9315024 en
dc.identifier.NAIST-ID 86630175 en
dc.identifier.NAIST-ID 86631215 en
dc.identifier.NAIST-ID 74652785 en
dc.identifier.NAIST-ID 73293045 en


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