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

Amir Haider, Muhammad Adnan Khan, Abdur Rehman, MuhibUr Rahman and Hyung Seok Kim

Article (2021)

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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 intru- sion 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 Cyber- security 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 frame- work 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 con- ventional algorithms. Furthermore, the proposed approach has not only research significance but also practical significance.

Uncontrolled Keywords

Security; DELM; intrusion detection system; machine learning

Subjects: 2700 Information technology > 2700 Information technology
Department: Department of Electrical Engineering
Funders: National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT), Strengthening R&D Capability Program of Sejong University
Grant number: 2019R1A4A1023746, 2019R1F1A1060799
PolyPublie URL: https://publications.polymtl.ca/9458/
Journal Title: Computers, Materials & Continua (vol. 66, no. 2)
Publisher: Tech Science Press
DOI: 10.32604/cmc.2020.013910
Official URL: https://doi.org/10.32604/cmc.2020.013910
Date Deposited: 13 Sep 2023 10:53
Last Modified: 06 Apr 2024 14:00
Cite in APA 7: Haider, A., Adnan Khan, M., Rehman, A., Rahman, M.U., & Seok Kim, H. (2021). A Real-Time Sequential Deep Extreme Learning Machine Cybersecurity Intrusion Detection System. Computers, Materials & Continua, 66(2), 1785-1798. https://doi.org/10.32604/cmc.2020.013910


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