Anomaly Detection Using Fuzzy Q-Learning Algorithm

dc.authorid Mat Kiah, Miss Laiha/0000-0002-1240-5406
dc.authorid Misra, Sanjay/0000-0002-3556-9331
dc.authorid Anuar, Nor Badrul/0000-0003-4380-5303
dc.authorscopusid 57221738247
dc.authorscopusid 22733749500
dc.authorscopusid 24833455600
dc.authorscopusid 56962766700
dc.authorwosid S.Band, Shahab/ABI-7388-2020
dc.authorwosid Mat Kiah, Miss Laiha/B-2767-2010
dc.authorwosid Misra, Sanjay/K-2203-2014
dc.authorwosid Anuar, Nor Badrul/B-3101-2010
dc.contributor.author Shamshirband, Shahaboddin
dc.contributor.author Anuar, Nor Badrul
dc.contributor.author Kiah, Miss Laiha Mat
dc.contributor.author Misra, Sanjay
dc.contributor.other Computer Engineering
dc.contributor.other Computer Engineering
dc.date.accessioned 2024-10-06T10:59:56Z
dc.date.available 2024-10-06T10:59:56Z
dc.date.issued 2014
dc.department Atılım University en_US
dc.department-temp [Shamshirband, Shahaboddin] Islamic Azad Univ, Dept Comp Sci, Chalous Branch, Chalous 46615397, Iran; [Anuar, Nor Badrul; Kiah, Miss Laiha Mat] Univ Malaya, Fac Comp Sci & Informat Technol, Kuala Lumpur 50603, Malaysia; [Misra, Sanjay] Atilim Univ, Dept Comp Engn, TR-06836 Ankara, Turkey en_US
dc.description Mat Kiah, Miss Laiha/0000-0002-1240-5406; Misra, Sanjay/0000-0002-3556-9331; Anuar, Nor Badrul/0000-0003-4380-5303 en_US
dc.description.abstract Wireless networks are increasingly overwhelmed by Distributed Denial of Service (DDoS) attacks by generating flooding packets that exhaust critical computing and communication resources of a victim's mobile device within a very short period of time. This must be protected. Effective detection of DDoS attacks requires an adaptive learning classifier, with less computational complexity, and an accurate decision making to stunt such attacks. In this paper, we propose an intrusion detection system called Fuzzy Q-learning (FQL) algorithm to protect wireless nodes within the network and target nodes from DDoS attacks to identify the attack patterns and take appropriate countermeasures. The FQL algorithm was trained and tested to establish its performance by generating attacks from the NSL-KDD and CAIDA DDoS Attack datasets during the simulation experiments. Experimental results show that the proposed FQL IDS has higher accuracy of detection rate than Fuzzy Logic Controller and Q-learning algorithm alone. en_US
dc.description.sponsorship University of Malaya, Malaysia [RG108-12ICT] en_US
dc.description.sponsorship This work is supported by the University of Malaya, Malaysia, under Research Grant RG108-12ICT. en_US
dc.description.woscitationindex Science Citation Index Expanded
dc.identifier.citationcount 9
dc.identifier.endpage 28 en_US
dc.identifier.issn 1785-8860
dc.identifier.issue 8 en_US
dc.identifier.scopus 2-s2.0-84908498737
dc.identifier.scopusquality Q1
dc.identifier.startpage 5 en_US
dc.identifier.uri https://hdl.handle.net/20.500.14411/9048
dc.identifier.volume 11 en_US
dc.identifier.wos WOS:000346148600001
dc.identifier.wosquality Q3
dc.institutionauthor Mısra, Sanjay
dc.institutionauthor Mısra, Sanjay
dc.language.iso en en_US
dc.publisher Budapest Tech en_US
dc.relation.ispartof Acta Polytechnica Hungarica en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.scopus.citedbyCount 22
dc.subject Intrusion detection en_US
dc.subject Fuzzy system en_US
dc.subject Reinforcement learning en_US
dc.subject Multi Agent System en_US
dc.title Anomaly Detection Using Fuzzy Q-Learning Algorithm en_US
dc.type Article en_US
dc.wos.citedbyCount 9
dspace.entity.type Publication
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