DOI QR코드

DOI QR Code

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

전기차 무선 충전 시스템에서 실시간 탐지를 위한 지능형 Bluetooth 침입 탐지 시스템 연구

  • 윤영훈 (동신대학교/신재생에너지전공) ;
  • 김대운 (동신대학교/융합정보보안전공) ;
  • 최정안 (동신대학교/융합정보보안전공) ;
  • 강승호 (동신대학교/융합정보보안전공)
  • Received : 2020.11.12
  • Accepted : 2020.12.18
  • Published : 2020.12.31

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.

IoT의 핵심 요소 기술 중 하나인 Bluetooth를 전기차 무선 충전 시스템에 사용하는 경우가 늘어나면서 이에 대한 보안 문제가 큰 이슈로 부각되고 있다. 무선 통신 기술인 Bluetooth에 보안을 강화하기 위한 다양한 기술적 노력이 있어 왔지만 여전히 다양한 공격 방법이 존재한다. 본 논문은 Bluetooth 시스템을 대상으로 대표적인 2가지 공격 방법을 지능적으로 탐지하기 위해 잘 알려진 Hidden Markov Model을 이용한 지능형 Bluetooth 침입 탐지 시스템을 제안한다. 제안 방법은 탐지의 정확성 이외에 실시간 탐지가 가능하도록 Bluetooth 전송 계층 프로토코인 H4의 패킷 타입과 전송 방향을 조합하고 이들의 시간상의 전개를 특징으로 사용한다. 데이터 수집 환경을 구성하고 실험을 통해 얻은 데이터를 대상으로 개발한 시스템의 성능을 분석한다.

Keywords

References

  1. P. Satam, S. Satam, and S. Hariri, Bluetooth Intrusion Detection System(BIDS), 2018 IEEE/ACS 15th International Conference on Computer Systems and Applications (AICCSA), October, 2018.
  2. J. A. Choi, D. W. Kim, and S. H. Kang, "An intelligent Bluetooth Intursion Detection Systgem using Hidden Markov Model", Proceeding of Cyber Security Conference at Honam, October, 2020.
  3. https://en.wikipedia.org/wiki/Bluetooth
  4. K. Haataja and P. Toivanen, "Two practical man-in-the-middle attacks on Bluetooth secure simple pairing and countermeasures in IEEE Transactions on Wireless Communications", vol. 9, no. 1, pp. 384-392, January 2010. https://doi.org/10.1109/TWC.2010.01.090935
  5. https://www.essaysusa.com/article/types-of-bluetooth-attacks
  6. J. Asharf, N. Moustafa, H. Khurshid, E. Debie, W. Haider and A. Wahab, "A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions", Electronics, 9(7), 2020.
  7. L.R. Rabiner, "A tutorialj on hidden Markov models and selectd applications in speech recognition", Proceedings of the IEEE, 77(2), pp.257-286, February 1989. https://doi.org/10.1109/5.18626
  8. https://github.com/crypt0b0y/BLUETOOTH-DOS-ATTACK-SCRIPT
  9. https://packages.debian.org/unstable/btscanner
  10. https://www.wireshark.org/
  11. https://github.com/KimiNewt/pyshark
  12. https://hmmlearn.readthedocs.io/en/latest/