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Classification of DoS Attacks in Smart Underwater Wireless Sensor Network

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Abstract

Due to deployment in sensitive military areas and other security applications, wireless networks are becoming a famous research spot in the field of computer science. To ratify the security and reliability in such kinds of application intrusion detection system can play an important role. There is a need of intrusion detection system, which has the capability to detect a large number of possible threats in wireless sensor networks. This article contains a customized dataset for smart underwater wireless sensor network that can be categorized into four types of DoS attacks (Blackhole, Grayhole, Flooding and Scheduling attacks). For the experimental purpose, since it is highly used in WSN, Low Energy Aware Cluster Hierarchy protocol has been used in this research work. Vector based forwarding provide scalable routing protocol to deploy smart underwater wireless sensor network. Using NS2 network simulator model a scheme has been defined to collect network traffic and create the dataset. Artificial Neural Network has been applied to train the dataset to classify it into different DoS attacks. Experimental work performed here gives high classification rate and accuracy for mentioning attacks with the help of proposed dataset. In future, suggested method can be useful for more attacks like Sybil/Wormhole presented in datalink layer as DoS attacks.

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References

  1. Marriwala, N., & Rathee, P. (2012). An approach to increase the wireless sensor network lifetime. In Proceedings of the world congress on information and communication technologies (WICT’12) (pp. 495–499). Trivandrum: IEEE.

  2. Gungor, V. C., Lu, B., & Hancke, G. P. (2010). Opportunities and challenges of wireless sensor networks in smart grid. IEEE Transactions on Industrial Electronics, 57(10), 3557–3564.

    Article  Google Scholar 

  3. Butun, S., Morgera, D., & Sankar, R. (2014). A survey of intrusion detection systems in wireless sensor networks. IEEE Communications Surveys and Tutorials, 16(1), 266–282.

    Article  Google Scholar 

  4. Farooq, N., Zahoor, I., Mandal, S., & Gulzar, T. (2014). Systematic analysis of DoS attacks in wireless sensor networks with wormhole injection. International Journal of Information and Computation Technology, 4(2), 173–182.

    Google Scholar 

  5. Rassam, M. A., Maarof, M. A., & Zainal, A. (2012). A survey of intrusion detection schemes in wireless sensor networks. American Journal of Applied Sciences, 9(10), 1636–1652.

    Article  Google Scholar 

  6. Heinzelman, W. R., Chandrakasan, A., & Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd IEEE annual hawaii international conference on system sciences (pp. 1–10). Maui, Hawaii, USA, January 2000.

  7. Enam, R. N., Tahir, M., & Qureshi, R. (2018). A survey of energy conservation mechanisms for dynamic cluster based wireless sensor networks. Mehran University Research Journal of Engineering and Technology, 37(2), 279.

    Article  Google Scholar 

  8. Tyagi, S., & Kumar, N. (2013). A systematic review on clustering and routing techniques based upon LEACH protocol for wireless sensor networks. Journal of Network and Computer Applications, 36(2), 623–645.

    Article  Google Scholar 

  9. Kumar, D. (2014). Performance analysis of energy efficient clustering protocols for maximizing lifetime of wireless sensor networks. IET Wireless Sensor Systems, 4(1), 9–16.

    Google Scholar 

  10. Enam, R. N., Ismat, N., & Farooq, F. (2017). Connectivity and coverage based grid-cluster size calculation in wireless sensor networks. Wireless Personal Communications (Springer, US), 95, 429–443.

    Article  Google Scholar 

  11. Miao, Y. M. (2015). Cluster-head election algorithm for wireless sensor networks based on LEACH protocol. Applied Mechanics and Materials, 738–739, 19–22.

    Article  Google Scholar 

  12. Taneja, S. (2015). An energy efficient approach using load distribution through LEACH-TLCH protocol. Journal of Network Communications and Emerging Technologies (JNCET), 5(3), 20–23.

    MathSciNet  Google Scholar 

  13. Garofalo, A., Di Sarno, C., & Formicola, V. (2013). Enhancing intrusion detection in wireless sensor networks through decision trees. In Dependable computing (pp. 1–15). Berlin: Springer.

  14. Patil, Shital., & Chaudhari, Sangita. (2016). DoS attack prevention technique in wireless sensor networks. Procedia Computer Science, 79, 715–721.

    Article  Google Scholar 

  15. Kaur, Rupinder., & Singh, Parminder. (2014). Review of black hole and grey hole attack. The International journal of Multimedia and Its Applications, 6, 35–45. https://doi.org/10.5121/ijma.2014.6603.

    Article  Google Scholar 

  16. Jangir, S. K., Hemrajani, N. (2016). A comprehensive review on detection of wormhole attack in MANET. In International conference on ICT in Business Industry and Government (ICTBIG) (pp. 1–8).

  17. Wang, S.-S., Yan, K.-Q., Wang, S.-C., & Liu, C.-W. (2011). An integrated intrusion detection system for cluster-based wireless sensor networks. Expert Systems with Applications, 38(12), 15234–15243.

    Article  Google Scholar 

  18. Xu, J., Wang, J., **e, S., Chen, W., & Kim, J.-U. (2013). Study on intrusion detection policy for wireless sensor networks. International Journal of Security and Its Applications, 7(1), 1–6.

    Google Scholar 

  19. Aggarwal, P., & Sharma, S. K. (2015). Analysis of KDD dataset attributes-class wise for intrusion detection. Procedia Computer Science, 57, 842–851.

    Article  Google Scholar 

  20. Everingham, M., Eslami, S. M. A., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2015). The pascal visual object classes challenge: A retrospective. International Journal of Computer Vision, 111(1), 98–136.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Technology, Nan**g University of Aeronautics and Astronautics, Nan**g, China

    Bilal Ahmad & Wang Jian

  2. Department of Computer Science, Sir Syed University of Engineering and Technology, Karachi, Pakistan

    Rabia Noor Enam

  3. Department of Computing, Middle East College, Muscat, Oman

    Ali Abbas

Authors
  1. Bilal Ahmad
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  2. Wang Jian
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  3. Rabia Noor Enam
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  4. Ali Abbas
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Correspondence to Bilal Ahmad.

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Ahmad, B., Jian, W., Enam, R.N. et al. Classification of DoS Attacks in Smart Underwater Wireless Sensor Network. Wireless Pers Commun 116, 1055–1069 (2021). https://doi.org/10.1007/s11277-019-06765-5

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