Title: Improving intrusion detection in the IoT with African vultures optimisation algorithm-based feature selection

Authors: Mohammed Alweshah; Ghadeer Ahmad Alhebaishan; Sofian Kassaymeh; Saleh Alkhalaileh; Mohammed Ababneh

Addresses: Prince Abdullah Bin Ghazi Faculty of Information and Communication Technology, Al-Balqa Applied University, Al-Salt, Jordan; Artificial Intelligence Department, Faculty of Information Technology, Aqaba University of Technology, Aqaba, Jordan ' Prince Abdullah Bin Ghazi Faculty of Information and Communication Technology, Al-Balqa Applied University, Al-Salt, Jordan ' Software Engineering Department, Faculty of Information Technology, Aqaba University of Technology, Aqaba, Jordan ' Prince Abdullah Bin Ghazi Faculty of Information and Communication Technology, Al-Balqa Applied University, Al-Salt, Jordan ' Prince Abdullah Bin Ghazi Faculty of Information and Communication Technology, Al-Balqa Applied University, Al-Salt, Jordan

Abstract: The security of the system may be jeopardised by unsecured data transmitted through IoT devices, and ensuring the reliability of data is critical to maintaining the integrity of information over the internet. To enhance the intrusion detection rate, several investigations have been conducted to develop methodologies capable of identifying the minimum required secure features. One such method is the use of the feature selection procedure with metaheuristic algorithms. In this study, the African vulture optimisation algorithm was used in two wrapper FS approaches to select the most secure features in IoT. The first approach used AVO, while the second employed OBL-AVO, a hybrid model combining AVO with opposition-based learning (OBL) to enhance exploration. Based on the outcomes, it was found that the OBL-AVO is superior to the AVO in enhancing FS. Furthermore, the proposed methods' were evaluated and compared to four recent approaches.

Keywords: intrusion detection; internet of things; IoT; feature selection; hybrid metaheuristics; African vultures optimisation algorithm; AVO; opposition-based learning; OBL.

DOI: 10.1504/IJDMMM.2024.140529

International Journal of Data Mining, Modelling and Management, 2024 Vol.16 No.3, pp.293 - 325

Received: 13 May 2023
Accepted: 22 Aug 2023
Published online: 22 Aug 2024 *

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