| DOI | Resolve DOI: https://doi.org/10.1109/GLOBECOM38437.2019.9013892 |
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| Author | Search for: Yang, Li; Search for: Moubayed, Abdallah; Search for: Hamieh, Ismail1; Search for: Shami, Abdallah |
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| Affiliation | - National Research Council Canada. Automotive and Surface Transportation
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| Format | Text, Article |
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| Conference | 2019 IEEE Global Communications Conference (GLOBECOM), December 9-12, 2019, Waikoloa, HI, USA |
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| Subject | intrusion detection system; CAN bus; VANET; autonomous vehicles; random forest; XGBoost; stacking; cyber security |
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| Abstract | The use of autonomous vehicles (AVs) is a promising technology in Intelligent Transportation Systems (ITSs) to improve safety and driving efficiency. Vehicle-to-everything (V2X) technology enables communication among vehicles and other infrastructures. However, AVs and Internet of Vehicles (IoV) are vulnerable to different types of cyber-attacks such as denial of service, spoofing, and sniffing attacks. In this paper, an intelligent intrusion detection system (IDS) is proposed based on tree-structure machine learning models. The results from the implementation of the proposed intrusion detection system on standard data sets indicate that the system has the ability to identify various cyber-attacks in the AV networks. Furthermore, the proposed ensemble learning and feature selection approaches enable the proposed system to achieve high detection rate and low computational cost simultaneously. |
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| Publication date | 2019-12-09 |
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| Publisher | IEEE |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 515608f2-4e95-4397-9910-cd8c21f60af3 |
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| Record created | 2021-09-03 |
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| Record modified | 2021-09-03 |
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