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ID Sequence Analysis for Intrusion Detection in the CAN bus using Long Short Term Memory Networks

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dc.contributor.author Araya, Kibrom Desta en
dc.contributor.author Ohira, Shuji en
dc.contributor.author Arai, Ismail en
dc.contributor.author Fujikawa, Kazutoshi en
dc.date.accessioned 2020-08-11T07:33:48Z en
dc.date.available 2020-08-11T07:33:48Z en
dc.date.issued 2020-08-04 en
dc.identifier.isbn 978-1-7281-4716-1 en
dc.identifier.uri http://hdl.handle.net/10061/14019 en
dc.description 2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops),23-27 March 2020,Austin, TX, USA, USA en
dc.description.abstract The number of computer controlled vehicles throughout the world is rising at a staggering speed. Even though this enhances the driving experience, it opens a new security hole in the automotive industry. To alleviate this issue, we are proposing an intrusion detection system (IDS) to the controller area network (CAN), which is the de facto communication standard of present-day vehicles. We implemented an IDS based on the analysis of ID sequences. The IDS uses a trained Long-Short Term Memory (LSTM) to predict an arbitration ID that will appear in the future by looking back to the last 20 packet arbitration IDs. The output from the LSTM network is a softmax probability of all the 42 arbitration IDs in our test car. The softmax probability is used in two approaches for IDS. In the first approach, a single arbitration ID is predicted by taking the class which has the highest softmax probability. This method only gave us an accuracy of 0.6. Applying this result in a real vehicle would give us a lot of false negatives, hence we devised a second approach that uses log loss as an anomaly signal. The evaluated log loss is compared with a predefined threshold to see if the result is in the anomaly boundary. Furthermore, We have tested our approach using insertion, drop and illegal ID attacks which greatly outperform the conventional method with practical F1 scores of 0.9, 0.84, and 1.0 respectively. en
dc.language.iso en en
dc.publisher IEEE en
dc.rights © 2020, IEEE ja
dc.subject LSTM en
dc.subject In-vehicle Network Security en
dc.subject Automotive en
dc.subject Intrusion Detection en
dc.subject CAN bus en
dc.title ID Sequence Analysis for Intrusion Detection in the CAN bus using Long Short Term Memory Networks en
dc.type.nii Conference Paper en
dc.contributor.transcription オオヒラ, シュウジ ja
dc.contributor.transcription アライ, イスマイル 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.jtitle 2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) en
dc.relation.doi 10.1109/PerComWorkshops48775.2020.9156250 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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