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Optimal deep learning driven intrusion detection in SDN-Enabled IoT environment
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Maray, Mohammed, Mesfer Alshahrani, Haya, A. Alissa, Khalid, Alotaibi, Najm, Gaddah, Abdulbaset, Meree, Ali, Othman, Mahmoud and Ahmed Hamza, Manar (2022) Optimal deep learning driven intrusion detection in SDN-Enabled IoT environment. Computers, Materials & Continua, 74 (3). pp. 6587-6604. doi:10.32604/cmc.2023.034176 ISSN 1546-2226.
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Official URL: http://doi.org/10.32604/cmc.2023.034176
Abstract
In recent years, wireless networks are widely used in different domains. This phenomenon has increased the number of Internet of Things (IoT) devices and their applications. Though IoT has numerous advantages, the commonly-used IoT devices are exposed to cyber-attacks periodically. This scenario necessitates real-time automated detection and the mitigation of different types of attacks in high-traffic networks. The Software-Defined Networking (SDN) technique and the Machine Learning (ML)-based intrusion detection technique are effective tools that can quickly respond to different types of attacks in the IoT networks. The Intrusion Detection System (IDS) models can be employed to secure the SDN-enabled IoT environment in this scenario. The current study devises a Harmony Search algorithm-based Feature Selection with Optimal Convolutional Autoencoder (HSAFS-OCAE) for intrusion detection in the SDN-enabled IoT environment. The presented HSAFS-OCAE method follows a three-stage process in which the Harmony Search Algorithm-based FS (HSAFS) technique is exploited at first for feature selection. Next, the CAE method is leveraged to recognize and classify intrusions in the SDN-enabled IoT environment. Finally, the Artificial Fish Swarm Algorithm (AFSA) is used to fine-tune the hyperparameters. This process improves the outcomes of the intrusion detection process executed by the CAE algorithm and shows the work’s novelty. The proposed HSAFS-OCAE technique was experimentally validated under different aspects, and the comparative analysis results established the supremacy of the proposed model.
Item Type: | Journal Article | ||||||||||||
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Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering | ||||||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||||||||
Library of Congress Subject Headings (LCSH): | Internet of things, Software-defined networking (Computer network technology), Machine learning, Information technology | ||||||||||||
Journal or Publication Title: | Computers, Materials & Continua | ||||||||||||
Publisher: | Tech Science Press | ||||||||||||
ISSN: | 1546-2226 | ||||||||||||
Official Date: | 28 December 2022 | ||||||||||||
Dates: |
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Volume: | 74 | ||||||||||||
Number: | 3 | ||||||||||||
Page Range: | pp. 6587-6604 | ||||||||||||
DOI: | 10.32604/cmc.2023.034176 | ||||||||||||
Status: | Peer Reviewed | ||||||||||||
Publication Status: | Published | ||||||||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||||||||
Date of first compliant deposit: | 7 February 2023 | ||||||||||||
Date of first compliant Open Access: | 7 February 2023 | ||||||||||||
RIOXX Funder/Project Grant: |
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