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IoT-Cloud Assisted Botnet Detection Using Rat Swarm Optimizer with Deep Learning

Saeed Masoud Alshahrani1, Fatma S. Alrayes2, Hamed Alqahtani3, Jaber S. Alzahrani4, Mohammed Maray5, Sana Alazwari6, Mohamed A. Shamseldin7, Mesfer Al Duhayyim8,*

1 Department of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra, Saudi Arabia
2 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
3 Department of Information Systems, College of Computer Science, Center of Artificial Intelligence, Unit of Cybersecurity, King Khalid University, Abha, Saudi Arabia
4 Department of Industrial Engineering, College of Engineering at Alqunfudah, Umm Al-Qura University, Saudi Arabia
5 Department of Information Systems, College of Computer Science, King Khalid University, Abha, Saudi Arabia
6 Department of Information Technology, College of Computers and Information Technology, Taif University, Taif P.O. Box 11099, Taif, 21944, Saudi Arabia
7 Department of Mechanical Engineering, Faculty of Engineering and Technology, Future University in Egypt, New Cairo, 11835, Egypt
8 Department of Computer Science, College of Sciences and Humanities-Aflaj, Prince Sattam bin Abdulaziz University, Saudi Arabia

* Corresponding Author: Mesfer Al Duhayyim. Email:

Computers, Materials & Continua 2023, 74(2), 3085-3100.


Nowadays, Internet of Things (IoT) has penetrated all facets of human life while on the other hand, IoT devices are heavily prone to cyberattacks. It has become important to develop an accurate system that can detect malicious attacks on IoT environments in order to mitigate security risks. Botnet is one of the dreadful malicious entities that has affected many users for the past few decades. It is challenging to recognize Botnet since it has excellent carrying and hidden capacities. Various approaches have been employed to identify the source of Botnet at earlier stages. Machine Learning (ML) and Deep Learning (DL) techniques are developed based on heavy influence from Botnet detection methodology. In spite of this, it is still a challenging task to detect Botnet at early stages due to low number of features accessible from Botnet dataset. The current study devises IoT with Cloud Assisted Botnet Detection and Classification utilizing Rat Swarm Optimizer with Deep Learning (BDC-RSODL) model. The presented BDC-RSODL model includes a series of processes like pre-processing, feature subset selection, classification, and parameter tuning. Initially, the network data is pre-processed to make it compatible for further processing. Besides, RSO algorithm is exploited for effective selection of subset of features. Additionally, Long Short Term Memory (LSTM) algorithm is utilized for both identification and classification of botnets. Finally, Sine Cosine Algorithm (SCA) is executed for fine-tuning the hyperparameters related to LSTM model. In order to validate the promising performance of BDC-RSODL system, a comprehensive comparison analysis was conducted. The obtained results confirmed the supremacy of BDC-RSODL model over recent approaches.


Cite This Article

S. M. Alshahrani, F. S. Alrayes, H. Alqahtani, J. S. Alzahrani, M. Maray et al., "Iot-cloud assisted botnet detection using rat swarm optimizer with deep learning," Computers, Materials & Continua, vol. 74, no.2, pp. 3085–3100, 2023.

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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