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

• Main Authors: Abd Elaziz, M. (Author), Alfadhli, S.A (Author), Al-qaness, M.A.A (Author), Alresheedi, S.S (Author), Dahou, A. (Author), Fatani, A. (Author), Lu, S. (Author)
• Format: Article
• Language: English
• Published: MDPI 2023
• Subjects: Cloud environments; CNNs; Computer crime; Convolutional neural networks; cyber security; Cyber security; Cybersecurity; Deep learning; Feature extraction; Growth optimizer; Growth Optimizer; Internet of thing; Internet of things; Internet of Things (IoT); Intrusion detection; intrusion detection system; Intrusion Detection Systems; Learning methods; Learning systems; Metaheuristic; metaheuristics; Network security; Optimization; Optimization algorithms; Optimizers; System models
• Online Access: View Fulltext in Publisher; View in Scopus

 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. © 2023 by the authors.