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A Complex Attacks Recognition Method in Wireless Intrusion Detection System

  • Guanlin Chen
  • Ying Wu
  • Kunlong Zhou
  • Yong ZhangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11204)

Abstract

During recent years, the challenge faced by wireless network security is getting severe with the rapid development of internet. However, due to the defects of wireless communication protocol and difference among wired networks, the existing intrusion prevention systems are seldom involved. This paper proposed a method of identifying complicated multistep attacks orienting to wireless intrusion detection system, which includes the submodules of alarm simplification, VTG generator, LAG generator, attack signature database, attack path resolver and complex attack evaluation. By means of introducing logic attack diagram and virtual topological graph, the attach path was excavated. The experimental result showed that this identification method is applicable to the real scene of wireless intrusion detection, which plays certain significance to predict attackers’ ultimate attack intention.

Keywords

Mobile Internet Wireless intrusion Multi-step attack 

Notes

Acknowledgements

This work was partially supported by Zhejiang Provincial Natural Science Foundation of China (No. LY16F020010), Hangzhou Science & Technology Development Project of China (No. 20150533B16, No. 20162013A08) and the 2016 National Undergraduate Training Programs for Innovation and Entrepreneurship, China (No. 201613021004).

References

  1. 1.
    Aparicio-Navarro, F.J., Kyriakopoulos, K.G., Parish, D.J.: An automatic and self-adaptive multi-layer data fusion system for WiFi attack detection. Int. J. Internet Technol. Secur. Trans. 5(1), 42–62 (2013)CrossRefGoogle Scholar
  2. 2.
    Afzal, Z., Rossebø, J., Talha, B., et al.: A wireless intrusion detection system for 802.11 networks. In: International Conference on IEEE Wireless Communications, Signal Processing and Networking (WiSPNET), pp. 828–834 (2016)Google Scholar
  3. 3.
    Victor, G.F., Carles, G., Helena, R.P.: A comparative study of anomaly detection techniques for smart city wireless sensor networks. Sensors 16(6), 868 (2016)CrossRefGoogle Scholar
  4. 4.
    Liang, H., Nannan, X., Erbuli, N., et al.: A multi-stage attack scenario recognition algorithm based on intelligent planning. Chin. J. Electron. 41(9), 1753–1759 (2013)Google Scholar
  5. 5.
    Shameli-Sendi, A., Louafi, H., He, W., et al.: A defense-centric model for multi-step attack damage cost evaluation. In: 2015 3rd International Conference on Future Internet of Things and Cloud (FiCloud), pp. 145–149. IEEE (2015)Google Scholar
  6. 6.
    Bi, K., Han, D., Wang, J.: K maximum probability attack paths dynamic generation algorithm. Comput. Sci. Inf. Syst. 13(2), 677–689 (2016)CrossRefGoogle Scholar
  7. 7.
    Wang, Z., Yuan, P., Huang, X., et al.: Research of a novel attack scenario constructing method. J. Southwest Univ. Sci. Technol. 31(1), 55–60 (2016)Google Scholar
  8. 8.
    Pan, S., Morris, T., Adhikari, U.: Developing a hybrid intrusion detection system using data mining for power systems. IEEE Trans. Smart Grid 6(6), 3104–3113 (2015)CrossRefGoogle Scholar
  9. 9.
    Julisch, K.: Mining alarm clusters to improve alarm handling efficiency. In: Proceedings of the 17th Annual Computer Security Applications Conference (ACSAC 2001), New Orleans, USA, pp. 12–21. IEEE Press (2001)Google Scholar
  10. 10.
    Jiang, Z., Zhao, J., Li, X.-Y., et al.: Rejecting the attack: source authentication for Wi-Fi management frames using CSI information. In: Proceedings of the 32nd IEEE Conference on Computer Communications (INFOCOM 2013), Turin, Italy, pp. 2544–2552. IEEE Press (2013)Google Scholar
  11. 11.
    Thangavel, M., Thangaraj, P.: Efficient hybrid network (wired and wireless) intrusion detection using statistical data streams and detection of clustered alerts. J. Comput. Sci. 7(9), 1318–1324 (2011)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Guanlin Chen
    • 1
    • 2
  • Ying Wu
    • 1
    • 2
  • Kunlong Zhou
    • 1
  • Yong Zhang
    • 1
    Email author
  1. 1.School of Computer and Computing ScienceZhejiang University City CollegeHangzhouPeople’s Republic of China
  2. 2.College of Computer Science and TechnologyZhejiang UniversityHangzhouPeople’s Republic of China

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