Open Access

ARTICLE

Resource Exhaustion Attack Detection Scheme for WLAN Using Artificial Neural Network

by Abdallah Elhigazi Abdallah¹, Mosab Hamdan², Shukor Abd Razak³, Fuad A. Ghalib³, Muzaffar Hamzah^2,*, Suleman Khan⁴, Siddiq Ahmed Babikir Ali⁵, Mutaz H. H. Khairi¹, Sayeed Salih⁶

1 Faculty of Engineering, Future University, Khartoum 10553, Sudan
2 Faculty of Computing and Informatics, Universiti Malaysia Sabah, Kota Kinabalu, 88400, Malaysia
3 Information Assurance and Security Research, School of Computing, Universiti Teknologi Malaysia, Johor, 81310, Malaysia
4 School of Psychology and Computer Science, University of Central Lancashire, Preston PR1 2HE, UK
5 Deaptement of Management Information Systems, College of Administrative Science, Applied Science University, Al Eker, 623, Bahrain
6 Deaptement of Information Technology, College of Computer and Information Sciences, King Saud University, Riyadh, 11461, Saudi Arabia

* Corresponding Author: Muzaffar Hamzah. Email:

Computers, Materials & Continua 2023, 74(3), 5607-5623. https://doi.org/10.32604/cmc.2023.031047

Received 08 April 2022; Accepted 15 September 2022; Issue published 28 December 2022

Abstract

IEEE 802.11 Wi-Fi networks are prone to many denial of service (DoS) attacks due to vulnerabilities at the media access control (MAC) layer of the 802.11 protocol. Due to the data transmission nature of the wireless local area network (WLAN) through radio waves, its communication is exposed to the possibility of being attacked by illegitimate users. Moreover, the security design of the wireless structure is vulnerable to versatile attacks. For example, the attacker can imitate genuine features, rendering classification-based methods inaccurate in differentiating between real and false messages. Although many security standards have been proposed over the last decades to overcome many wireless network attacks, effectively detecting such attacks is crucial in today’s real-world applications. This paper presents a novel resource exhaustion attack detection scheme (READS) to detect resource exhaustion attacks effectively. The proposed scheme can differentiate between the genuine and fake management frames in the early stages of the attack such that access points can effectively mitigate the consequences of the attack. The scheme is built through learning from clustered samples using artificial neural networks to identify the genuine and rogue resource exhaustion management frames effectively and efficiently in the WLAN. The proposed scheme consists of four modules which make it capable to alleviates the attack impact more effectively than the related work. The experimental results show the effectiveness of the proposed technique by gaining an 89.11% improvement compared to the existing works in terms of detection.

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Keywords

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