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Jensen–Shannon Divergence Based Independent Component Analysis to Detect and Prevent Black Hole Attacks in Healthcare WSN

  • A. John Clement SunderEmail author
  • A. Shanmugam
Article
  • 8 Downloads

Abstract

The black hole attack is an adverse issue in Wireless Sensor Networks (WSNs). Research for detection and circumvention of the black hole attack is underway. However, the false alarm rate, amount of time required to identify the black hole attack nodes in network has not reduced. To overcome such limitations, the Jensen–Shannon Divergence Based Independent Component Analysis (JDICA) technique is proposed in this paper. This technique is introduced with the application of Jensen–Shannon Divergence estimation in Independent Component Analysis model on the contrary to existing works, in order to achieve higher black hole detection accuracy in healthcare WSN. The JDICA technique identifies the black hole attack by analyzing the physiological data gathered from biomedical sensors. The proposed JDICA technique carries out attack detection based on sensor nodes behaviors such as energy, trust and cooperative count. It determines the dependence among the nodes, based on the independent probability distribution functions and mutual probability function by using the Jensen–Shannon Divergence. The divergence result enables JDICA technique to detect black hole attacks with greater accuracy, and helps to quarantine the malicious node from the network by broadcasting the isolation message to all sensor nodes in the network. Hence, JDICA technique enhances the detection of black hole attack nodes as compared to state-of-the-art works, thereby increasing the packet delivery ratio and reducing delay. The JDICA technique simulation is done considering the metrics such as detection rate, detection time, false alarm rate, and packet delivery ratio with respect to a varied number of sensor nodes and data packets. Simulation results makes it apparent that the JDICA technique improves the detection rate and minimizes the detection time of the black hole attack when compared to state-of-the-art works.

Keywords

Black hole attack Cooperative count Energy Healthcare WSN Isolation Jensen–Shannon Divergence Sensor node Trust value 

Notes

References

  1. 1.
    Wang, Y., Zhang, M., & Shu, W. (2018). An emerging intelligent optimization algorithm based on trust sensing model for wireless sensor networks. EURASIP Journal on Wireless Communications and Networking, 2018(1), 145.CrossRefGoogle Scholar
  2. 2.
    Ren, J., Zhang, Y., Zhang, K., & Shen, X. (2016). Adaptive and channel-aware detection of selective forwarding attacks in wireless sensor networks. IEEE Transactions on Wireless Communications, 15(5), 3718–3731.CrossRefGoogle Scholar
  3. 3.
    Pavan Kumar Guptha, Y., & Madhu, M. (2017). Improving security and detecting black hole attack in wireless sensor network. International Journal of Professional Engineering Studies, 8(5), 260–265.Google Scholar
  4. 4.
    Prathapani, A., Santhanam, L., & Agrawal, D. P. (2013). Detection of blackhole attack in a Wireless Mesh Network using intelligent honeypot agents. The Journal of Supercomputing, 64(3), 777–804.CrossRefGoogle Scholar
  5. 5.
    Arya, M. (2014). BFO based optimized positioning for black hole attack mitigation in WSN. International Journal of Engineering Trends and Technology (IJETT), 14(1), 29–34.CrossRefGoogle Scholar
  6. 6.
    Mathur, A., Newe, T., & Rao, M. (2016). Defence against black hole and selective forwarding attacks for medical WSNs in the IoT. Sensors, 16(1), 118.CrossRefGoogle Scholar
  7. 7.
    Pathak, G. R., Patil, S. H., & Tryambake, J. S. (2014). Efficient and trust based black hole attack detection and prevention in WSN. International Journal of Computer Science and Business Informatics, 14(2), 93–103.Google Scholar
  8. 8.
    Shamshirband, S., Patel, A., Anuar, N. B., Kiah, M. L. M., & Abraham, A. (2014). Cooperative game theoretic approach using fuzzy Q-learning for detecting and preventing intrusions in wireless sensor networks. Engineering Applications of Artificial Intelligence, 32, 228–241.CrossRefGoogle Scholar
  9. 9.
    Taylor, V. F., & Fokum, D. T. (2014). Mitigating black hole attacks in wireless sensor networks using node-resident expert systems. In 2014 wireless telecommunications symposium (pp. 1–7). IEEE.Google Scholar
  10. 10.
    Babu, M. R., Dian, S. M., Chelladurai, S., & Palaniappan, M. (2015). Proactive alleviation procedure to handle black hole attack and its version. The Scientific World Journal,.  https://doi.org/10.1155/2015/715820.Google Scholar
  11. 11.
    Sonal, Kiran Narang. (2013). Black hole attack detection using fuzzy logic. International Journal of Science and Research (IJSR), 2(8), 222–225.Google Scholar
  12. 12.
    Kumar, S., & Sangwan, S. (2015). A survey of black hole detection techniques in WSNs. International Journal of Advanced Research in Computer and Communication Engineering, 4(5), 557–562.CrossRefGoogle Scholar
  13. 13.
    Virmani, D., Soni, A., & Batra, N. (2014). Reliability analysis to overcome black hole attack in wireless sensor network. arXiv preprint arXiv:1401.2540.
  14. 14.
    Karuppiah, A. B., Dalfiah, J., Yuvashri, K., & Rajaram, S. (2015). An improvised hierarchical black hole detection algorithm in wireless sensor networks. In International confernce on innovation information in computing technologies (pp. 1–7). IEEE.Google Scholar
  15. 15.
    Panos, C., Ntantogian, C., Malliaros, S., & Xenakis, C. (2017). Analyzing, quantifying, and detecting the blackhole attack in infrastructure-less networks. Computer Networks, 113, 94–110.CrossRefGoogle Scholar
  16. 16.
    Ozay, M., Esnaola, I., Vural, F. T. Y., Kulkarni, S. R., & Poor, H. V. (2016). Machine learning methods for attack detection in the smart grid. IEEE Transactions on Neural Networks and Learning Systems, 27(8), 1773–1786.MathSciNetCrossRefGoogle Scholar
  17. 17.
    Aljumah, A., & Ahanger, T. A. (2017). Futuristic method to detect and prevent blackhole attack in wireless sensor networks. International Journal of Computer Science and Network Security (IJCSNS), 17(2), 194.Google Scholar
  18. 18.
    Maleh, Y., Ezzati, A., Qasmaoui, Y., & Mbida, M. (2015). A global hybrid intrusion detection system for wireless sensor networks. Procedia Computer Science, 52, 1047–1052.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringBannari Amman Institute of TechnologySathyamangalamIndia
  2. 2.Department of Electronics and Communication EngineeringSNS College of TechnologyCoimbatoreIndia

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