Open Access

ARTICLE

A Real-Time Sequential Deep Extreme Learning Machine Cybersecurity Intrusion Detection System

by Amir Haider¹, Muhammad Adnan Khan², Abdur Rehman³, Muhib Ur Rahman⁴, Hyung Seok Kim^1,*

1 Department of Intelligent Mechatronics Engineering, Sejong University, Seoul, 05006, Korea
2 Department of Computer Science, Lahore Garrison University, Lahore, 54000, Pakistan
3 School of Computer Science, National College of Business Administration & Economics, Lahore, 54000, Pakistan
4 Department of Electrical Engineering, Polytechnique Montreal, Montreal, QC H3T 1J4, Canada

* Corresponding Author: Hyung Seok Kim. Email:

(This article belongs to the Special Issue: Machine Learning-based Intelligent Systems: Theories, Algorithms, and Applications)

Computers, Materials & Continua 2021, 66(2), 1785-1798. https://doi.org/10.32604/cmc.2020.013910

Received 26 August 2020; Accepted 28 September 2020; Issue published 26 November 2020

Abstract

In recent years, cybersecurity has attracted significant interest due to the rapid growth of the Internet of Things (IoT) and the widespread development of computer infrastructure and systems. It is thus becoming particularly necessary to identify cyber-attacks or irregularities in the system and develop an efficient intrusion detection framework that is integral to security. Researchers have worked on developing intrusion detection models that depend on machine learning (ML) methods to address these security problems. An intelligent intrusion detection device powered by data can exploit artificial intelligence (AI), and especially ML, techniques. Accordingly, we propose in this article an intrusion detection model based on a Real-Time Sequential Deep Extreme Learning Machine Cybersecurity Intrusion Detection System (RTS-DELM-CSIDS) security model. The proposed model initially determines the rating of security aspects contributing to their significance and then develops a comprehensive intrusion detection framework focused on the essential characteristics. Furthermore, we investigated the feasibility of our proposed RTS-DELM-CSIDS framework by performing dataset evaluations and calculating accuracy parameters to validate. The experimental findings demonstrate that the RTS-DELM-CSIDS framework outperforms conventional algorithms. Furthermore, the proposed approach has not only research significance but also practical significance.

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Citations