Open Access

ARTICLE

Intelligent Intrusion Detection System for Industrial Internet of Things Environment

R. Gopi¹, R. Sheeba², K. Anguraj³, T. Chelladurai⁴, Haya Mesfer Alshahrani⁵, Nadhem Nemri^6,*, Tarek Lamoudan⁷

1 Department of Computer Science and Engineering, Dhanalakshmi Srinivasan Engineering College, Perambalur, 621 212, India
2 Department of Computer Science and Engineering, K. Ramakrishnan College of Engineering, Tiruchirappalli, 621112, India
3 Department of Electronics and Communication Engineering, Sona College of Technology, Salem, 636005, India
4 Department of Electronics and Communication Engineering, PSNA College of Engineering and Technology, Dindigul, 624622, India
5 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, 11671, Saudi Arabia
6 Department of Information Systems, College of Science & Arts at Mahayil, King Khalid University, Muhayel Aseer, 62529, Saudi Arabia
7 Department of Mathematics, College of Science & Arts at Mahayil, King Khalid University, Muhayel Aseer, 62529, Saudi Arabia

* Corresponding Author: Nadhem Nemri. Email:

Computer Systems Science and Engineering 2023, 44(2), 1567-1582. https://doi.org/10.32604/csse.2023.025216

Received 16 November 2021; Accepted 05 January 2022; Issue published 15 June 2022

Abstract

Rapid increase in the large quantity of industrial data, Industry 4.0/5.0 poses several challenging issues such as heterogeneous data generation, data sensing and collection, real-time data processing, and high request arrival rates. The classical intrusion detection system (IDS) is not a practical solution to the Industry 4.0 environment owing to the resource limitations and complexity. To resolve these issues, this paper designs a new Chaotic Cuckoo Search Optimization Algorithm (CCSOA) with optimal wavelet kernel extreme learning machine (OWKELM) named CCSOA-OWKELM technique for IDS on the Industry 4.0 platform. The CCSOA-OWKELM technique focuses on the design of feature selection with classification approach to achieve minimum computation complexity and maximum detection accuracy. The CCSOA-OWKELM technique involves the design of CCSOA based feature selection technique, which incorporates the concepts of chaotic maps with CSOA. Besides, the OWKELM technique is applied for the intrusion detection and classification process. In addition, the OWKELM technique is derived by the hyperparameter tuning of the WKELM technique by the use of sunflower optimization (SFO) algorithm. The utilization of CCSOA for feature subset selection and SFO algorithm based hyperparameter tuning leads to better performance. In order to guarantee the supreme performance of the CCSOA-OWKELM technique, a wide range of experiments take place on two benchmark datasets and the experimental outcomes demonstrate the promising performance of the CCSOA-OWKELM technique over the recent state of art techniques.

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Keywords

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