Vol.73, No.2, 2022, pp.3199-3217, doi:10.32604/cmc.2022.027212
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:
Received 13 January 2022; Accepted 19 April 2022; Issue published 16 June 2022
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.
Internet of Things; deep learning security; DDoS attack; botnet
Cite This Article
T. Ahamed Ahanger, A. Aldaej, M. Atiquzzaman, I. Ullah and M. Yousuf Uddin, "Securing consumer internet of things for botnet attacks: deep learning approach," Computers, Materials & Continua, vol. 73, no.2, pp. 3199–3217, 2022.
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