Enhancing Intrusion Detection Systems for IoT and Cloud Environments Using a Growth Optimizer Algorithm and Conventional Neural Networks

Abdulaziz Fatani; Abdelghani Dahou; Mohamed Abd Elaziz; Mohammed A A Al-Qaness; Songfeng Lu; Saad Ali Alfadhli; Shayem Saleh Alresheedi

doi:10.3390/s23094430

Enhancing Intrusion Detection Systems for IoT and Cloud Environments Using a Growth Optimizer Algorithm and Conventional Neural Networks

Sensors (Basel). 2023 Apr 30;23(9):4430. doi: 10.3390/s23094430.

Authors

Abdulaziz Fatani^{1

2}, Abdelghani Dahou³, Mohamed Abd Elaziz^{4

5

6

7}, Mohammed A A Al-Qaness⁸, Songfeng Lu^{9

10}, Saad Ali Alfadhli¹¹, Shayem Saleh Alresheedi¹²

Affiliations

¹ School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China.
² Computer Science Department, Umm Al-Qura University, Makkah 24381, Saudi Arabia.
³ Faculty of Computer Sciences and Mathematics, Ahmed Draia University, Adrar 01000, Algeria.
⁴ Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt.
⁵ Artificial Intelligence Research Center (AIRC), Ajman University, Ajman 346, United Arab Emirates.
⁶ Department of Artificial Intelligence Science and Engineering, Galala University, Suze 435611, Egypt.
⁷ Department of Electrical and Computer Engineering, Lebanese American University, Byblos 13-5053, Lebanon.
⁸ College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua 321004, China.
⁹ Hubei Engineering Research Center on Big Data Security, School of Cyber Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.
¹⁰ Shenzhen Huazhong University of Science and Technology Research Institute, Shenzhen 518057, China.
¹¹ Department of Computer Techniques Engineering, Imam Al-Kadhum College, Baghdad 10081, Iraq.
¹² War College, National Defense University, Riyadh 12211, Saudi Arabia.

Abstract

Intrusion detection systems (IDS) play a crucial role in securing networks and identifying malicious activity. This is a critical problem in cyber security. In recent years, metaheuristic optimization algorithms and deep learning techniques have been applied to IDS to improve their accuracy and efficiency. Generally, optimization algorithms can be used to boost the performance of IDS models. Deep learning methods, such as convolutional neural networks, have also been used to improve the ability of IDS to detect and classify intrusions. In this paper, we propose a new IDS model based on the combination of deep learning and optimization methods. First, a feature extraction method based on CNNs is developed. Then, a new feature selection method is used based on a modified version of Growth Optimizer (GO), called MGO. We use the Whale Optimization Algorithm (WOA) to boost the search process of the GO. Extensive evaluation and comparisons have been conducted to assess the quality of the suggested method using public datasets of cloud and Internet of Things (IoT) environments. The applied techniques have shown promising results in identifying previously unknown attacks with high accuracy rates. The MGO performed better than several previous methods in all experimental comparisons.

Keywords: CNNs; Growth Optimizer; Internet of Things (IoT); cyber security; intrusion detection system; metaheuristics.

Grants and funding

This work is supported by the National Key R&D Program of China under Grand No. 2021YFB2012202 and the Hubei Provincial Science and Technology Major Project of China under Grant No. 2020AEA011 and the Key Research & Development Plan of Hubei Province of China under Grant No. 2021BAA171, 2021BAA038, 2020BAB100 and the project of Science, Technology and Innovation Commission of Shenzhen Municipality of China under Grant No. JCYJ20210324120002006 and JSGG20210802153009028, the 2021 Industrial Technology Basic Public Service Platform Project of the Ministry of Industry and Information Technology of PRC under Grand No. 2021-0171-1-1.