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Hybrid Anomaly Detection by Using Clustering for Wireless Sensor Network

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Abstract

Performance of wireless sensor network are highly prone to network anomalies particularly to misdirection attacks and blackhole attacks. Therefor intrusion detection system has a key role in WSN and it’s essential in security application. However the identification of active attacks is cumbersome in many cases particularly for remote sensing applications. This paper proposes hybrid anomaly detection method for misdirection and blackhole attacks by employing K-medoid customized clustering technique. A synthetic dataset was established by defining network parameters and threshold values were obtained to detect the anomalies. Experimental work was performed on network simulator (NS-2) and R studio. The proposed algorithm successfully detect the hybrid anomalies with high accuracy. This work is suitable for hybrid anomaly detection including misdirection and blackhole attacks in wireless environment.

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Authors and Affiliations

  1. Department of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Bilal Ahmad & Wang Jian

  2. College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China

    Zain Anwar Ali

  3. Electronic Engineering Department, Sir Syed University of Engineering and Technology, Karachi, Pakistan

    Zain Anwar Ali

  4. Biomedical Engineering Department, Sir Syed University of Engineering and Technology, Karachi, Pakistan

    Sania Tanvir

  5. Department of Computer Science, University of Karachi, Karachi, Pakistan

    M. Sadiq Ali Khan

Authors
  1. Bilal Ahmad
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  2. Wang Jian
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  3. Zain Anwar Ali
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  4. Sania Tanvir
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  5. M. Sadiq Ali Khan
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Correspondence to Bilal Ahmad.

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Ahmad, B., Jian, W., Ali, Z.A. et al. Hybrid Anomaly Detection by Using Clustering for Wireless Sensor Network. Wireless Pers Commun 106, 1841–1853 (2019). https://doi.org/10.1007/s11277-018-5721-6

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  • DOI: https://doi.org/10.1007/s11277-018-5721-6

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