Advertisement

Detecting Unknown Attacks in Wireless Sensor Networks Using Clustering Techniques

  • Z. Banković
  • J. M. Moya
  • J. C. Vallejo
  • D. Fraga
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6678)

Abstract

Wireless sensor networks are usually deployed in unattended environments. This is the main reason why the update of security policies upon identifying new attacks cannot be done in a timely fashion, which gives enough time to attackers to make significant damage. Thus, it is of great importance to provide protection from unknown attacks. However, existing solutions are mostly concentrated on known attacks. In order to tackle this issue, we propose a machine learning solution for anomaly detection along with the feature extraction process that tries to detect temporal and spatial inconsistencies in the sequences of sensed values and the routing paths used to forward these values to the base station. The data produced in the presence of an attacker are treated as outliers, and detected using clustering techniques. The techniques are coupled with a reputation system, isolating in this way the compromised nodes. The proposal exhibits good performances in detecting and confining previously unseen attacks.

Keywords

wireless sensor networks unknown attacks clustering reputation system 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Corral, G., Armengol, E., Fornells, A., Golobardes, E.: Explanations of unsupervised learning clustering applied to data security analysis. Neurocomputing 72(13-15), 2754–2762 (2009)CrossRefGoogle Scholar
  2. 2.
    Herrero, A., Corchado, E., Pellicer, M.A., Abraham, A.: MOVIH-IDS: A mobile-visualization hybrid intrusion detection system. Neurocomputing 72(13-15), 2775–2784 (2009)CrossRefGoogle Scholar
  3. 3.
    Krontiris, I., Giannetsos, T., Dimitriou, T.: LIDeA: A Distributed Lightweight Intrusion Detection Architecture for Sensor Networks. In: 4th International Conference on Security and Privacy for Communication Networks. ACM, New York (2008)Google Scholar
  4. 4.
    Hai, T.H., Khan, F., Huh, E.-n.: Hybrid Intrusion Detection System for Wireless Sensor Networks. In: Gervasi, O., Gavrilova, M.L. (eds.) ICCSA 2007, Part II. LNCS, vol. 4706, pp. 383–396. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  5. 5.
    Onat, I., Miri, A.: A Real-Time Node-Based Traffic Anomaly Detection Algorithm for Wireless Sensor Networks. In: Systems Communications, pp. 422–427. IEEE Press, Los Alamitos (2005)Google Scholar
  6. 6.
    Wallenta, C., Kim, J., Bentley, P.J., Hailes, S.: Detecting Interest Cache Poisoning in Sensor Networks using an Artificial Immune Algorithm. Appl. Intell. 32, 1–26 (2010)CrossRefGoogle Scholar
  7. 7.
    Kaplantzis, S., Shilton, A., Mani, N., Sekercioglu, Y.A.: Detecting Selective Forwarding Attacks in WSNs using Support Vector Machines. In: Int. Sensors, Sensor Networks and Inf. Proc. Conf., pp. 335–340. IEEE Press, Los Alamitos (2007)Google Scholar
  8. 8.
    Loo, C.E., Ng, M.Y., Leckie, C., Palaniswami, M.: Intrusion Detection for Routing Attacks in Sensor Networks. Int. J. of Dist. Sens. Net. 2(4), 313–332 (2006)CrossRefGoogle Scholar
  9. 9.
    Moya, J.M., Araujo, A., Bankovic, Z., de Goyeneche, J.M., Vallejo, J.C., Malagon, P., Villanueva, D., Fraga, D., Romero, E., Blesa, J.: Improving Security for SCADA Sensor Networks with Reputation Systems and SOMs. Sensors 9, 9380–9397 (2009)CrossRefGoogle Scholar
  10. 10.
    Banković, Z., Moya, J.M., Araujo, A., Fraga, D., Vallejo, J.C., de Goyeneche, J.M.: Distributed Intrusion Detection System for WSNs based on a Reputation System coupled with Kernel Self-Organizing Maps. Int. Comp. Aided Design 17(2), 87–102 (2010)Google Scholar
  11. 11.
    Rieck, K., Laskov, P.: Linear Time Computation of Similarity for Sequential Data. J. Mach. Learn. Res. 9, 23–48 (2008)zbMATHGoogle Scholar
  12. 12.
    Muñoz, A., Muruzábal, J.: Self-Organizing Maps for Outlier Detection. Neurocomputing 18(1-3), 33–60 (1998)CrossRefGoogle Scholar
  13. 13.
    Roosta, T.G.: Attacks and Defenses on Ubiquitous Sensor Networks, Ph. D. Dissertation, University of California at Berkeley (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Z. Banković
    • 1
  • J. M. Moya
    • 1
  • J. C. Vallejo
    • 1
  • D. Fraga
    • 1
  1. 1.Dep. Ingeniería ElectrónicaUniversidad Politécnica de MadridMadridSpain

Personalised recommendations