Hybrid Anomaly Detection by Using Clustering for Wireless Sensor Network

  • Bilal Ahmad
  • Wang Jian
  • Zain Anwar Ali
  • Sania Tanvir
  • M. Sadiq Ali Khan
Article

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.

Keywords

Hybrid anomaly Clustering WSN Black hole Misdirection attack 

Notes

References

  1. 1.
    Nishani, L., & Biba, M. (2016). Machine learning for intrusion detection in MANET: A state-of-the-art survey. Journal of Intelligent Information Systems, 46(2), 391–407.CrossRefGoogle Scholar
  2. 2.
    Pachauria, G., & Sharma, S. (2015). Anomaly detection in medical wireless sensor networks using machine learning algorithms. Procedia Computer Science, 70, 325–333.CrossRefGoogle Scholar
  3. 3.
    Alsheikh, M. A., Lin, S., Niyato, D., & Tan, H.-P. (2015). Machine learning in wireless sensor networks: Algorithms, strategies, and applications. Procedia Computer Science, 70, 325–333.CrossRefGoogle Scholar
  4. 4.
    Kavitha, P., & Usha, M. (2014). Cluster based anomaly detection in wireless LAN. International Journal of Computer Trends and Technology (IJCTT), 12(5), 227–230.CrossRefGoogle Scholar
  5. 5.
    Kalaiselvan, K., & Singh, G. (2015). Detection and isolation of black hole attack in wireless sensor networks. International Journal of Innovative Research in Science, Engineering and Technology, 4(5), 3516–3524.Google Scholar
  6. 6.
    Kaur, R., Sharma, D., & Kaur, N. (2013). Comparative analysis of leach and its descendant protocols in wireless sensor network. International Journal of P2P Network Trends and Technology, 3(1), 51–55.Google Scholar
  7. 7.
    Almomani, I., Al-Kasasbeh, B., & AL-Akhras, M. (2016). WSN-DS: A dataset for intrusion detection systems in wireless sensor networks. Journal of Sensors, 2016, Article ID 4731953.Google Scholar
  8. 8.
    Shi, Qiong, Qin, Li, Song, Lipeng, Zhang, Rongping, & Jia, Yanfeng. (2017). A dynamic programming model for internal attack detection in wireless sensor networks. Discrete Dynamics in Nature and Society, 2017, 1–9.CrossRefMATHGoogle Scholar
  9. 9.
    Hou, X., Lei, C.-U., & Kwok, Y.-K. (2017). OP-DCI: A riskless K-means clustering for influential user identification in MOOC forum. In 16th IEEE international conference on machine learning and applications (ICMLA) (pp. 936–939).Google Scholar
  10. 10.
    Alipour, H., Al-Nashif, Y. B., Satam, P., & Hariri, S. (2015). Wireless anomaly detection based on IEEE 802.11 behavior analysis. IEEE Transactions on Information Forensics and Security, 10(10), 2158–2170.CrossRefGoogle Scholar
  11. 11.
    Garcia-Font, V., Garrigues, C., & Rifà-Pous, H. (2016). A comparative study of anomaly detection techniques for smart city wireless sensor networks. In Lu, R. (Ed.) Sensors, Vol. 16, no. 6, Basel, Switzerland.Google Scholar
  12. 12.
    Shah, Z., & Patel, R. (2016). Misdirection attack in wireless sensor network: A survey. International Journal for Technological Research in Engineering, 3(9), 2044–2047.Google Scholar
  13. 13.
    Gao, H., Wu, R., Cao, M., & Zhang, C. (2014). Detection and defense technology of blackhole attacks in wireless sensor network. In X. Sun et al. (Eds.), Algorithms and architectures for parallel processing (pp. 601–610). Cham: Springer.Google Scholar
  14. 14.
    Syarif, I., Prugel-Bennett, A., & Wills, G. (2012). Unsupervised clustering approach for network anomaly. In R. Benlamri (Ed.), Networked digital technologies. Communications in computer and information science (Vol. 293, pp. 135–145). Berlin: Springer.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Bilal Ahmad
    • 1
  • Wang Jian
    • 1
  • Zain Anwar Ali
    • 2
    • 3
  • Sania Tanvir
    • 4
  • M. Sadiq Ali Khan
    • 5
  1. 1.Department of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjingChina
  2. 2.College of Automation EngineeringNanjing University of Aeronautics and AstronauticsNanjingChina
  3. 3.Electronic Engineering DepartmentSir Syed University of Engineering and TechnologyKarachiPakistan
  4. 4.Biomedical Engineering DepartmentSir Syed University of Engineering and TechnologyKarachiPakistan
  5. 5.Department of Computer ScienceUniversity of KarachiKarachiPakistan

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