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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

A lightweight intelligent intrusion detection system for industrial internet of things using deep learning algorithm

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Author(s):
Mendonca, Robson V. [1] ; Silva, Juan C. [2] ; Rosa, Renata L. [1] ; Saadi, Muhammad [3] ; Rodriguez, Demostenes Z. [1] ; Farouk, Ahmed [4]
Total Authors: 6
Affiliation:
[1] Univ Fed Lavras, Dept Comp Sci, BR-37202618 Lavras, MG - Brazil
[2] Pontifical Catholic Univ, Dept Sci, Lima - Peru
[3] Univ Cent Punjab, Dept Elect Engn, Fac Engn, Lahore - Pakistan
[4] South Valley Univ, Fac Comp & Artificial Intelligence, Dept Comp Sci, Hurghada - Egypt
Total Affiliations: 4
Document type: Journal article
Source: EXPERT SYSTEMS; DEC 2021.
Web of Science Citations: 0
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. (AU)

FAPESP's process: 15/24496-0 - Evaluation of the service of communication operators using the voice Quality Index
Grantee:Demostenes Zegarra Rodriguez
Support Opportunities: Regular Research Grants
FAPESP's process: 18/26455-8 - Audio-Visual Speech Processing by Machine Learning
Grantee:Miguel Arjona Ramírez
Support Opportunities: Regular Research Grants