Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 고병진 | - |
dc.date.accessioned | 2022-08-22T23:56:34Z | - |
dc.date.available | 2022-08-22T23:56:34Z | - |
dc.date.issued | 2021-07 | - |
dc.identifier.citation | IEEE ACCESS, v. 9, Page. 101289-101299 | en_US |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.uri | https://doaj.org/article/fd0aa14b2de14e3b8817fb2a1276b94b | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/172532 | - |
dc.description.abstract | Malicious attacks reduce the benefits of cooperative adaptive cruise control (CACC) such as safety, driving convenience, traffic flow, and fuel efficiency, by destabilizing the stability. To reinforce the resiliency of a CACC based platoon of connected and automated vehicles (CAVs), this work investigates a detection method for malicious information attacks in the platoon. In this work, we propose an attack detection method, called LMID (long short-term memory (LSTM) based malicious information detection). We consider two attack models: correlated attacks and non-correlated attacks. In our attack scenarios, one of the platoon members attacks the platoon using the attack models. Using PLEXE, a well-known platoon simulator, we develop a simulation framework to implement attack scenarios and evaluate the proposed detection method. LMID is trained depending on the length of input data and analyzed under various scenarios regarding platoon trajectories, attack types, and an emergency brake case. We have shown that without fast detection of such attacks, crashes may happen within a platoon. The simulation results demonstrate that LMID detects the malicious information attacks with higher than 96% accuracy and the attacks are detected very quickly. The performance evaluation indicates the superiority of the proposed detection method under various circumstances. | en_US |
dc.description.sponsorship | This work was supported in part by the Institute for Information and Communications Technology Promotion (IITP) through the Korean Government (MSIP) under Grant B0101-15-0557 (the Resilient Cyber-Physical Systems Research), and in part by the Ministry of Science and ICT (MSIT), South Korea, through the Grand Information and Communication Technology Research Center Support Program under Grant IITP-2020-0-101741 [supervised by the Institute for Information and Communications Technology Planning and Evaluation (IITP)]. | en_US |
dc.language.iso | en | en_US |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | en_US |
dc.subject | Attack model | en_US |
dc.subject | LSTM based attack detection | en_US |
dc.subject | malicious information | en_US |
dc.subject | platoon | en_US |
dc.title | An Approach to Detecting Malicious Information Attacks for Platoon Safety | en_US |
dc.type | Article | en_US |
dc.relation.volume | 9 | - |
dc.identifier.doi | 10.1109/ACCESS.2021.3095480 | - |
dc.relation.page | 101289-101299 | - |
dc.relation.journal | IEEE ACCESS | - |
dc.contributor.googleauthor | Ko, Byungjin | - |
dc.contributor.googleauthor | Son, Sang Hyuk | - |
dc.relation.code | 2021000011 | - |
dc.sector.campus | E | - |
dc.sector.daehak | COLLEGE OF ENGINEERING SCIENCES[E] | - |
dc.identifier.pid | byungjinko | - |
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