Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 강제원 | * |
dc.date.accessioned | 2016-08-29T12:08:46Z | - |
dc.date.available | 2016-08-29T12:08:46Z | - |
dc.date.issued | 2016 | * |
dc.identifier.isbn | 9781509016983 | * |
dc.identifier.issn | 1550-2252 | * |
dc.identifier.other | OAK-19154 | * |
dc.identifier.uri | https://dspace.ewha.ac.kr/handle/2015.oak/231808 | - |
dc.description.abstract | In this paper, we propose a novel intrusion detection technique using a deep neural network (DNN). In the proposed technique, in-vehicle network packets exchanged between electronic control units (ECU) are trained to extract low- dimensional features and used for discriminating normal and hacking packets. The features perform in high efficient and low complexity because they are generated directly from a bitstream over the network. The proposed technique monitors an exchanging packet in the vehicular network while the feature are trained off-line, and provides a real-time response to the attack with a significantly high detection ratio in our experiments. © 2016 IEEE. | * |
dc.language | English | * |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | * |
dc.title | A novel intrusion detection method using deep neural network for in-vehicle network security | * |
dc.type | Conference Paper | * |
dc.relation.volume | 2016-July | * |
dc.relation.index | SCOPUS | * |
dc.relation.journaltitle | IEEE Vehicular Technology Conference | * |
dc.identifier.doi | 10.1109/VTCSpring.2016.7504089 | * |
dc.identifier.scopusid | 2-s2.0-84979753503 | * |
dc.author.google | Kang M.-J. | * |
dc.author.google | Kang J.-W. | * |
dc.contributor.scopusid | 강제원(56367466400) | * |
dc.date.modifydate | 20240322125621 | * |