Open Access

ARTICLE

Blockchain Assisted Optimal Machine Learning Based Cyberattack Detection and Classification Scheme

by Manal Abdullah Alohali¹, Muna Elsadig¹, Fahd N. Al-Wesabi^2,*, Mesfer Al Duhayyim³, Anwer Mustafa Hilal⁴, Abdelwahed Motwakel⁴

1 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
2 Department of Computer Science, College of Science & Art at Mahayil, King Khalid University, Abha, 62529, Saudi Arabia
3 Department of Computer Science, College of Sciences and Humanities-Aflaj, Prince Sattam bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia
4 Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University Al-Kharj, 16278, Saudi Arabia

* Corresponding Author: Fahd N. Al-Wesabi. Email:

Computer Systems Science and Engineering 2023, 46(3), 3583-3598. https://doi.org/10.32604/csse.2023.037545

Received 08 November 2022; Accepted 02 February 2023; Issue published 03 April 2023

Abstract

With recent advancements in information and communication technology, a huge volume of corporate and sensitive user data was shared consistently across the network, making it vulnerable to an attack that may be brought some factors under risk: data availability, confidentiality, and integrity. Intrusion Detection Systems (IDS) were mostly exploited in various networks to help promptly recognize intrusions. Nowadays, blockchain (BC) technology has received much more interest as a means to share data without needing a trusted third person. Therefore, this study designs a new Blockchain Assisted Optimal Machine Learning based Cyberattack Detection and Classification (BAOML-CADC) technique. In the BAOML-CADC technique, the major focus lies in identifying cyberattacks. To do so, the presented BAOML-CADC technique applies a thermal equilibrium algorithm-based feature selection (TEA-FS) method for the optimal choice of features. The BAOML-CADC technique uses an extreme learning machine (ELM) model for cyberattack recognition. In addition, a BC-based integrity verification technique is developed to defend against the misrouting attack, showing the innovation of the work. The experimental validation of BAOML-CADC algorithm is tested on a benchmark cyberattack dataset. The obtained values implied the improved performance of the BAOML-CADC algorithm over other techniques.

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Keywords

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