AI-based techniques for DDoS Attack Detection in WSN: a systematic literature review

Mohammed, Al-Naeem and Mohammed, Ashikur Rahman and Abubakar, Adamu and Rahman, M.M. Hafizur (2020) AI-based techniques for DDoS Attack Detection in WSN: a systematic literature review. Journal of Computer Science, 16 (6). pp. 848-855. ISSN 1549-3636 E-ISSN 1552-6607

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Abstract

Wireless Sensor Networks (WSNs) are currently being used in various industries such as healthcare, engineering, the environment and so on. Security is a significant issue for WSN due to its infrastructure and limited physical security. Distributed Denial of Service (DDoS) is one of the most vulnerable attacks that can be defined as attacks launched from multiple ends into a set of legitimate sensor nodes in the WSN to drain their inadequate energy resources. Nowadays, Artificial intelligence techniques are performing better accuracy than the traditional methods to detect intrusion for the various attack. This Systematic Literature Review (SLR) attempts to investigate the current status of DDoS detection techniques and to identify the most capable and effective detection system using artificial intelligence to detect distributed DoS attack. Preferred Reporting Item for Systematic Review and Meta-Analysis (PRISMA) statement is used to conduct this review. Based on 15 out of 983 that met inclusion criteria, Support Vector Machine (SVM) and Artificial Neural Network (ANN) is the most used AI-based techniques to detect distributed denial of service attack in the wireless sensor network. The performance of AI techniques-based detection system for DDoS attack in WSN is remarkable.

Item Type:	Article (Journal)
Additional Information:	7132/81267
Uncontrolled Keywords:	Artificial Intelligence, Distributed Denial of Service, Wireless Sensor Network, SLR
Subjects:	Q Science > QA Mathematics > QA76 Computer software
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Information and Communication Technology > Department of Computer Science Kulliyyah of Information and Communication Technology > Department of Computer Science
Depositing User:	Dr Adamu Abubakar
Date Deposited:	22 Jul 2020 16:31
Last Modified:	10 Dec 2020 16:11
URI:	http://irep.iium.edu.my/id/eprint/81267

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