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Journal of Cybersecurity and Information Management
Volume 0 , Issue 2, PP: 65-74 , 2019 | Cite this article as | XML |PDF

Title

Mitigating DDoS Attacks in Wireless Sensor Networks using Heuristic Feature Selection with Deep Learning Model

  Abdul Rahaman Wahab Sait 1 * ,   Irina Pustokhina 2 ,   M. Ilayaraja 3

1  King Faisal University, Kingdom of Saudi Arabia
    (asait@kfu.edu.sa )

2  Plekhanov Russian University of Economics, Moscow, Russia
    ( ivpustokhina@yandex.ru)

3  Department of Computer Science and Information Technology, Kalasalingam Academy of Research and Education, Krishnankoil, India
    (ilayaraja.m@klu.ac.in)


Doi   :   https://doi.org/10.54216/JCIM.000106


Abstract :

A wireless sensor network (WSN) encompasses a massive set of sensors with limited abilities for gathering sensitive data. Since security is a significant issue in WSN, there is a possibility of different types of attacks. In Distributed Denial of Service (DDOS) attack, the malicious node can adapt to several attacks, namely flooding, black hole, warm hole, etc., to interrupt the working of the WSN. The recently developed deep learning (DL) models can effectively detect DDoS attacks in the network. Therefore, this article proposes a heuristic feature selection with a Deep Learning-based DDoS (HFSDL-DDoS) attack detection model in WSN. The proposed HFSDL-DDoS technique intends to identify and categorize the occurrence of DDoS attacks in WSN. In addition, the HFSDL-DDoS technique involves the immune clonal genetic algorithm (ICGA) based feature selection (FS) approach to improve the detection performance. Moreover, a fruit fly algorithm (FFA) with bidirectional long, short-term memory (BiLSTM) based classification model is employed. The experimental analysis of the HFSDL-DDoS technique is performed, and the results are examined interms of several performance measures. The resultant experimental results pointed out the betterment of the HFSDL-DDoS technique over the other techniques.

Keywords :

WSN , Security , DDoS attacks , Deep learning , Feature selection , Metaheuristics

References :

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Cite this Article as :
Style #
MLA Abdul Rahaman Wahab Sait, Irina Pustokhina, M. Ilayaraja. "Mitigating DDoS Attacks in Wireless Sensor Networks using Heuristic Feature Selection with Deep Learning Model." Journal of Cybersecurity and Information Management, Vol. 0, No. 2, 2019 ,PP. 65-74 (Doi   :  https://doi.org/10.54216/JCIM.000106)
APA Abdul Rahaman Wahab Sait, Irina Pustokhina, M. Ilayaraja. (2019). Mitigating DDoS Attacks in Wireless Sensor Networks using Heuristic Feature Selection with Deep Learning Model. Journal of Journal of Cybersecurity and Information Management, 0 ( 2 ), 65-74 (Doi   :  https://doi.org/10.54216/JCIM.000106)
Chicago Abdul Rahaman Wahab Sait, Irina Pustokhina, M. Ilayaraja. "Mitigating DDoS Attacks in Wireless Sensor Networks using Heuristic Feature Selection with Deep Learning Model." Journal of Journal of Cybersecurity and Information Management, 0 no. 2 (2019): 65-74 (Doi   :  https://doi.org/10.54216/JCIM.000106)
Harvard Abdul Rahaman Wahab Sait, Irina Pustokhina, M. Ilayaraja. (2019). Mitigating DDoS Attacks in Wireless Sensor Networks using Heuristic Feature Selection with Deep Learning Model. Journal of Journal of Cybersecurity and Information Management, 0 ( 2 ), 65-74 (Doi   :  https://doi.org/10.54216/JCIM.000106)
Vancouver Abdul Rahaman Wahab Sait, Irina Pustokhina, M. Ilayaraja. Mitigating DDoS Attacks in Wireless Sensor Networks using Heuristic Feature Selection with Deep Learning Model. Journal of Journal of Cybersecurity and Information Management, (2019); 0 ( 2 ): 65-74 (Doi   :  https://doi.org/10.54216/JCIM.000106)
IEEE Abdul Rahaman Wahab Sait, Irina Pustokhina, M. Ilayaraja, Mitigating DDoS Attacks in Wireless Sensor Networks using Heuristic Feature Selection with Deep Learning Model, Journal of Journal of Cybersecurity and Information Management, Vol. 0 , No. 2 , (2019) : 65-74 (Doi   :  https://doi.org/10.54216/JCIM.000106)