A lightweight intelligent intrusion detection system for industrial internet of things using deep learning algorithms. Issue 5 (11th December 2021)

• Record Type: Journal Article
• Authors: Mendonça, Robson V.; Silva, Juan C.; Rosa, Renata L.; Saadi, Muhammad; Rodriguez, Demostenes Z.; Farouk, Ahmed
• Other Names: Sharma Rohit guestEditor.; Gupta Deepak guestEditor.; Maseleno Andino guestEditor.; Peng Sheng‐Lung guestEditor.; Menon Varun G. guestEditor.; Khosravi Reza guestEditor.; Jolfaei Alireza guestEditor.; Kumar Akshi guestEditor.; P Vinod guestEditor.
• Is Part Of: Expert systems. Volume 39:Issue 5(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-12-11
• Subjects: cybersecurity attacks -- deep learning -- industrial internet‐of‐things -- intrusion detection system; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12917
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: With the substantial industrial growth, the industrial internet of things (IIoT) and many IoT avenues have emerged. However, the existing industrial architectures are still inefficient to deal with advanced security issues due to the distributed and distensible nature of the network IIoT communication networks. Therefore, solutions for improving intelligent decision‐making actions to the IIoT are sorely necessary. Thus, in this paper, the main cybersecurity attacks are predicted by applying a deep learning model. The various security and integrity features such as the DoS, malevolent operation, data type probing, spying, scanning, intrusion detection, brute force, web attacks, and wrong setup is analysed and detected by a novel sparse evolutionary training (SET) based prediction model. To scrutinize the conduct of the proposed SET‐based prediction model, evaluation parameters, such as, precision, accuracy, recall, and F1 score are measured and compared to other state‐of‐the‐art algorithms, in which the proposed SET‐based model achieved an average accuracy of 0.99% for an average testing time of 2.29 ms. Results reveal that the proposed model improved the attack detection accuracy by an average of 6.25% when compared with the other state‐of‐the‐art machine learning models in a real scenario of IoT security in Industry 4.0.