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Securing Consumer Internet of Things for Botnet Attacks: Deep Learning Approach

Tariq Ahamed Ahanger1,*, Abdulaziz Aldaej1, Mohammed Atiquzzaman2, Imdad Ullah1, Mohammed Yousuf Uddin1

1 College of Computer Engineering and Science, Prince Sattam bin Abdulaziz University Al-Kharj, 11942, Saudi Arabia
2 School of Computer Science, University of Oklahoma Norman, 73019-6151, United States

* Corresponding Author: Tariq Ahamed Ahanger. Email: email

Computers, Materials & Continua 2022, 73(2), 3199-3217. https://doi.org/10.32604/cmc.2022.027212

Abstract

DDoS attacks in the Internet of Things (IoT) technology have increased significantly due to its spread adoption in different industrial domains. The purpose of the current research is to propose a novel technique for detecting botnet attacks in user-oriented IoT environments. Conspicuously, an attack identification technique inspired by Recurrent Neural networks and Bidirectional Long Short Term Memory (BLRNN) is presented using a unique Deep Learning (DL) technique. For text identification and translation of attack data segments into tokenized form, word embedding is employed. The performance analysis of the presented technique is performed in comparison to the state-of-the-art DL techniques. Specifically, Accuracy (98.4%), Specificity (98.7%), Sensitivity (99.0%), F-measure (99.0%) and Data loss (92.36%) of the presented BLRNN detection model are determined for identifying 4 attacks over Botnet (Mirai). The results show that, although adding cost to each epoch and increasing computation delay, the bidirectional strategy is more superior technique model over different data instances.

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Cite This Article

APA Style
Ahanger, T.A., Aldaej, A., Atiquzzaman, M., Ullah, I., Uddin, M.Y. (2022). Securing consumer internet of things for botnet attacks: deep learning approach. Computers, Materials & Continua, 73(2), 3199-3217. https://doi.org/10.32604/cmc.2022.027212
Vancouver Style
Ahanger TA, Aldaej A, Atiquzzaman M, Ullah I, Uddin MY. Securing consumer internet of things for botnet attacks: deep learning approach. Comput Mater Contin. 2022;73(2):3199-3217 https://doi.org/10.32604/cmc.2022.027212
IEEE Style
T.A. Ahanger, A. Aldaej, M. Atiquzzaman, I. Ullah, and M.Y. Uddin "Securing Consumer Internet of Things for Botnet Attacks: Deep Learning Approach," Comput. Mater. Contin., vol. 73, no. 2, pp. 3199-3217. 2022. https://doi.org/10.32604/cmc.2022.027212



cc Copyright © 2022 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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