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Design and Evaluation of a Weighted Intrusion Detection Method for VANETs

VANETs을 위한 가중치 기반 침입탐지 방법의 설계 및 평가

  • Received : 2011.04.18
  • Accepted : 2011.06.01
  • Published : 2011.06.30

Abstract

With the rapid proliferation of wireless networks and mobile computing applications, the landscape of the network security has greatly changed recently. Especially, Vehicular Ad Hoc Networks maintaining network topology with vehicle nodes of high mobility are self-organizing Peer-to-Peer networks that typically have short-lasting and unstable communication links. VANETs are formed with neither fixed infrastructure, centralized administration, nor dedicated routing equipment, and vehicle nodes are moving, joining and leaving the network with very high speed 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 connection without centralized control. In this paper, we propose a weighted intrusion detection method using rough set that can identify malicious behavior of vehicle node's activity and detect intrusions efficiently in VANETs. The performance of the proposed scheme is evaluated by a simulation study in terms of intrusion detection rate and false alarm rate for the threshold of deviation number ${\epsilon}$.

무선 네트워크와 모바일 컴퓨팅 응용의 급속한 보급과 더불어, 최근 네트워크 보안의 배경도 많은 변화를 가져왔다. 특히 이동성이 높은 차량 노드들로 네트워크 위상을 유지하는 차량 애드 혹 네트워크(Vehicular Ad Hoc Networks: VANETs)는 일반적으로 불안정한 통신 링크를 갖는 자기 조직화 P2P 망으로, 고정된 인프라 구조나 중앙 통제 라우팅 장비 없이 자동으로 망을 구성하고, 시간에 따라 고속으로 이동하며 망에 결합하거나 이탈하는 개방 망이므로 중앙 집중 제어 없이 누구나 접속이 허용되기 때문에 네트워크상에 해로운 비정상 행위 노드들에 대한 침입에 매우 취약하다. 본 논문에서는 VANETs에서의 노드들의 활동에 대한 비정상 행위를 효율적으로 식별하여 침입을 탐지할 수 있는 러프집합을 이용한 가중치 기반 침입탐지 방법을 제안하고, 그 성능을 모의실험을 통해 임계 허용 오차 ${\epsilon}$에 대한 비정상 행위로 인한 침입 탐지율과 거짓 경고율로 평가한다.

Keywords

References

  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. H. Deng et al., "Agent-based Distributed Intrusion Detection Methodology for MANETs," Proc. of the 2006 International Conference on Security & Management, pp. 200-206, 2006.
  7. P. Brutch and C. Ko, "Challenges in Intrusion Detection for Wireless Ad-Hoc Networks", Proc. of the 2003 Symposium on Applications and the Internet Workshop, pp. 368-373, 2003.
  8. B. Chen, L. Fu and D. Liu, "Efficient Anomaly Monitoring over Moving Object Trajectory Streams", KDD'2009, pp. 159-167, 2009.
  9. The PReVENT WILLWARN subproject website. [Online]. Available: http://www.prevent-ip.org/en/ prevent_subprojects/safe_speedandsafe_following/willwarn/
  10. The PReVENT project website. [Online]. Available: http://www.prevent-ip.org/
  11. 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.
  12. C. Guizani and A. Al-Fuqaha, "Constructing an Efficient Mobility Profile of Ad-Hoc for Mobility-Pattern-Based Anomaly Detection in MANET," GLOBECOM'2006, pp. 1-5, 2006.
  13. 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.
  14. Z. Pawlak, Rough Sets Theoretical Aspects of Reasoning about Data, Kluwer Academic Pub., 1991.