한국인터넷방송통신학회논문지 (The Journal of the Institute of Internet, Broadcasting and Communication)

  • 제10권2호
  • /
  • Pages.77-83
  • /
  • 2010
  • /
  • 2289-0238(pISSN)
  • /
  • 2289-0246(eISSN)
한국인터넷방송통신학회 (The Institute of Internet, Broadcasting and Communication)
권호 표지

VANETs의 보안을 위한 비정상 행위 탐지 방법

An Anomaly Detection Method for the Security of VANETs

  • 투고 : 2010.03.17
  • 발행 : 2010.04.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

차량 애드 혹 망 (Vehicular Ad Hoc Networks: VANETs)은 일반적으로 이동성이 높은 차량 노드들로 구성되어 매우 짧은 시간 망 위상이 지속되므로 불안정한 통신 링크를 갖는 자기 조직화 P2P 망이다. VANETs은 고정된 인프라 구조나 중앙 통제 라우팅 장비 없이 자동적으로 망구조를 구성하고, 차량 노드들은 시간에 따라 고속으로 이동하며 망에 결합하거나 이탈하는 개방 망이므로 중앙 집중식 제어 없이 누구나 접속을 허용하기 때문에 망상에 해롭고 비정상 행위 노드들에 대한 침입에 매우 취약하다. 본 논문에서는 이러한 VANETs에서의 노드들의 활동에 대한 비정상 행위를 효율적으로 식별할 수 있는 러프집합기반 비정상 행위 탐지방법을 제안하고, 그 성능을 모의실험을 통해 임계 허용 오차에 대한 비정상 행위 탐지율과 거짓 경고율로 평가하였다.

Vehicular Ad Hoc Networks are self-organizing Peer-to-Peer networks that typically have highly mobile vehicle nodes, moving at high speeds, very short-lasting and unstable communication links. VANETs are formed without fixed infrastructure, central administration, and dedicated routing equipment, and network nodes are mobile, joining and leaving the network over time. So, VANET-security is very vulnerable for the intrusion of malicious and misbehaving nodes in the network, since VANETs are mostly open networks, allowing everyone connect, without centralized control. In this paper, we propose a rough set based anomaly detection method that efficiently identify malicious behavior of vehicle node activities in these VANETs, and the performance of a proposed scheme is evaluated by a simulation in terms of anomaly detection rate and false alarm rate for the threshold ${\epsilon}$.

키워드

참고문헌

  1. B. Karp and H. T. Kung, "GPSR: greedy perimeter stateless routing for wireless networks," Proc. in Mobile Computing and Networking, pp. 243–254, 2000.
  2. C. Perkins and P. Bhagwat, "Highly dynamic destination-sequenced distance-vector routing (DSDV) for mobile computers," Proc. in ACM SIG-COMM'94 Conference on Communications Architectures, Protocols and Applications, pp. 234–244, 1994.
  3. W. Franz and C. Maihoefer, "Geographical addressing and forwarding in fleetnet," Proc. in Mobile Computing and Networking, 2002.
  4. H. Fuessler, M. Mauve, H. Hartenstein, M. Kaesemann, and D. Vollmer, "A Comparison of Routing Strategies for Vehicular Ad Hoc Networks," Department of Computer Science, University of Mannheim, Mannheim, Tech. Rep. TR-3-2002, 2002.
  5. T. Leinmueller, E. Schoch, F. Kargl, and C. Maihoefer, "Influence of Falsified Position Data on Geographic Ad-hoc Routing," Proc. in ESAS 2005, 2nd European Workshop on Security and Privacy in Ad hoc and Sensor Networks, 2005.
  6. C. Cai et al., "Constructing an Efficient Mobility Profile of Ad-Hoc for Mobility-Pattern-Based Anomaly Detection in MANET," Proc. in the Global Telecommunications Conference, pp. 1-5, 2006.
  7. The PReVENT WILLWARN subproject website. [Online]. Available: http://www.prevent-ip.org/en/prevent_subprojects/safe_speedandsafe_following/willwarn/
  8. The PReVENT project website. [Online]. Available: http://www.prevent-ip.org/
  9. Hongmei Deng et al., "Agent-based Distributed Intrusion Detection Methodology for MANETs," Proc. of the 2006 International Conference on Security & Management, pp. 200-206, 2006.
  10. D. Stern et al., "A General Cooperative Intrusion Detection Architecture for MANETs," Proc. of the 3rd IEEE International Workshop on Information Assurance, pp. 57-70, 2005.
  11. R.Jensen and Q. Shen, Fuzzy-Rough Sets for Descriptive Dimensionality Reduction, Proc. of the 11th International Conference on Fuzzy Systems, pp. 29-34., 2002.
  12. Z. Pawlak, Rough Sets Theoretical Aspects of Reasoning about Data, Kluwer Academic Pub., 1991.