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LSTM-Based Intrusion Detection System for In-Vehicle Can Bus Communications
http://hdl.handle.net/10061/14221
http://hdl.handle.net/10061/14221495eeb6d-15a6-4d8b-9aed-bb9747788f07
Item type | 学術雑誌論文 / Journal Article(1) | |||||
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公開日 | 2021-03-05 | |||||
タイトル | ||||||
タイトル | LSTM-Based Intrusion Detection System for In-Vehicle Can Bus Communications | |||||
言語 | ||||||
言語 | eng | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | Automobiles | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | Fuzzing | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | Protocols | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | Intrusion detection | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | Machine learning | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | Computer crime | |||||
資源タイプ | ||||||
資源タイプ | journal article | |||||
アクセス権 | ||||||
アクセス権 | metadata only access | |||||
著者 |
Hossain, Md Delwar
× Hossain, Md Delwar× Doudou, Fall× Kadobayashi, Youki |
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抄録 | ||||||
内容記述タイプ | Abstract | |||||
内容記述 | 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. | |||||
書誌情報 |
en : IEEE Access 巻 8, p. 185489-185502, 発行日 2020-10-07 |
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出版者 | ||||||
出版者 | IEEE | |||||
ISSN | ||||||
収録物識別子タイプ | ISSN | |||||
収録物識別子 | 2169-3536 | |||||
出版者版DOI | ||||||
関連タイプ | isReplacedBy | |||||
識別子タイプ | DOI | |||||
関連識別子 | https://doi.org/10.1109/ACCESS.2020.3029307 | |||||
出版者版URI | ||||||
関連タイプ | isReplacedBy | |||||
識別子タイプ | URI | |||||
関連識別子 | https://ieeexplore.ieee.org/document/9216166 | |||||
権利 | ||||||
権利情報 | 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/ |