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http://dx.doi.org/10.33778/kcsa.2020.20.5.011

An Intelligent Bluetooth Intrusion Detection System for the Real Time Detection in Electric Vehicle Charging System  

Yun, Young-Hoon (동신대학교/신재생에너지전공)
Kim, Dae-Woon (동신대학교/융합정보보안전공)
Choi, Jung-Ahn (동신대학교/융합정보보안전공)
Kang, Seung-Ho (동신대학교/융합정보보안전공)
Publication Information
Convergence Security Journal / v.20, no.5, 2020 , pp. 11-17 More about this Journal
Abstract
With the increase in cases of using Bluetooth devices used in the electric vehicle charging systems, security issues are also raised. Although various technical efforts have beed made to enhance security of bluetooth technology, various attack methods exist. In this paper, we propose an intelligent Bluetooth intrusion detection system based on a well-known machine learning method, Hidden Markov Model, for the purpose of detecting intelligently representative Bluetooth attack methods. The proposed approach combines packet types of H4, which is bluetooth transport layer protocol, and the transport directions of the packet firstly to represent the behavior of current traffic, and uses the temporal deployment of these combined types as the final input features for detecting attacks in real time as well as accurate detection. We construct the experimental environment for the data acquisition and analysis the performance of the proposed system against obtained data set.
Keywords
Electric Vehicle Charging System; Bluetooth; Intrusion Detection System; Information Security; Hidden Markov Model; Real Time Detection; Bluetooth Attack;
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