A Study on Attack Pattern Generation and Hybrid MR-IDS for In-Vehicle Network
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kang, D.M. | - |
dc.contributor.author | Yoon, S.H. | - |
dc.contributor.author | Shin, D.K. | - |
dc.contributor.author | Yoon, Young | - |
dc.contributor.author | Kim, H.M. | - |
dc.contributor.author | Jang, S.H. | - |
dc.date.accessioned | 2021-09-02T03:42:00Z | - |
dc.date.available | 2021-09-02T03:42:00Z | - |
dc.date.created | 2021-08-18 | - |
dc.date.issued | 2021 | - |
dc.identifier.issn | 0000-0000 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/15872 | - |
dc.description.abstract | The CAN (Controller Area Network) bus, which transmits and receives ECU control information in vehicle, has a critical risk of external intrusion because there is no standardized security system. Recently, the need for IDS (Intrusion Detection System) to detect external intrusion of CAN bus is increasing, and high accuracy and real-Time processing for intrusion detection are required. In this paper, we propose Hybrid MR (Machine learning and Ruleset)-IDS based on machine learning and ruleset to improve IDS performance. For high accuracy and detection rate, feature engineering was conducted based on the characteristics of the CAN bus, and the generated features were used in detection step. The proposed Hybrid MR-IDS can cope to various attack patterns that have not been learned in previous, as well as the learned attack patterns by using both advantages of rule set and machine learning. In addition, by collecting CAN data from an actual vehicle in driving and stop state, five attack scenarios including physical effects during all driving cycle are generated. Finally, the Hybrid MR-IDS proposed in this paper shows an average of 99% performance based on F1-score. © 2021 IEEE. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.subject | Control system synthesis | - |
dc.subject | Intrusion detection | - |
dc.subject | Machine learning | - |
dc.subject | Vehicles | - |
dc.subject | Cans (controller area network) | - |
dc.subject | Control information | - |
dc.subject | External intrusion | - |
dc.subject | Feature engineerings | - |
dc.subject | IDS(intrusion detection system) | - |
dc.subject | In-vehicle networks | - |
dc.subject | Performance based | - |
dc.subject | Realtime processing | - |
dc.subject | Network security | - |
dc.title | A Study on Attack Pattern Generation and Hybrid MR-IDS for In-Vehicle Network | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Yoon, Young | - |
dc.identifier.doi | 10.1109/ICAIIC51459.2021.9415261 | - |
dc.identifier.scopusid | 2-s2.0-85105448633 | - |
dc.identifier.wosid | 000674469600059 | - |
dc.identifier.bibliographicCitation | 3rd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2021, pp.291 - 294 | - |
dc.relation.isPartOf | 3rd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2021 | - |
dc.citation.title | 3rd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2021 | - |
dc.citation.startPage | 291 | - |
dc.citation.endPage | 294 | - |
dc.type.rims | ART | - |
dc.type.docType | Proceedings Paper | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.subject.keywordPlus | Control system synthesis | - |
dc.subject.keywordPlus | Intrusion detection | - |
dc.subject.keywordPlus | Machine learning | - |
dc.subject.keywordPlus | Vehicles | - |
dc.subject.keywordPlus | Cans (controller area network) | - |
dc.subject.keywordPlus | Control information | - |
dc.subject.keywordPlus | External intrusion | - |
dc.subject.keywordPlus | Feature engineerings | - |
dc.subject.keywordPlus | IDS(intrusion detection system) | - |
dc.subject.keywordPlus | In-vehicle networks | - |
dc.subject.keywordPlus | Performance based | - |
dc.subject.keywordPlus | Realtime processing | - |
dc.subject.keywordPlus | Network security | - |
dc.subject.keywordAuthor | CAN | - |
dc.subject.keywordAuthor | Hybrid MR-IDS | - |
dc.subject.keywordAuthor | Machine Learning | - |
dc.subject.keywordAuthor | Network Intrusion | - |
dc.subject.keywordAuthor | Ruleset | - |
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