Multi-agent system for an adaptive real time intrusion detection system

Wathiq Laftah Abd-Ali Al-Yaseen (P65615)

Please use this identifier to cite or link to this item: https://ptsldigital.ukm.my/jspui/handle/123456789/513259

Full metadata record

DC Field	Value	Language
dc.contributor.advisor	Zulaiha Ali Othman, Assoc. Prof. Dr.	-
dc.contributor.author	Wathiq Laftah Abd-Ali Al-Yaseen (P65615)	-
dc.date.accessioned	2023-10-16T04:35:04Z	-
dc.date.available	2023-10-16T04:35:04Z	-
dc.date.issued	2016-12-23	-
dc.identifier.other	ukmvital:96521	-
dc.identifier.uri	https://ptsldigital.ukm.my/jspui/handle/123456789/513259	-
dc.description	The increased speed of network data traffic and growing number of attacks on computer networks have become challenges for an intrusion detection system’s (IDS) such as long processing time, low detection rates and adaptively on new attacks. Most researches attempted to overcome the obstacles by improving the algorithm or improving the IDS’s architectural design either as distributed or parallel to make an IDS faster, especially multi-agent system (MAS) approach which is a well-known of an autonomous distributed parallel solution. However, researches are still focusing on improving the detection rates and less focus on processing time and adaptive IDS. Various algorithms have been proposed to handle the detection rate. However, Support Vector Machine (SVM) and Extreme Learning Machine (ELM) have shown the best solution with high ability to classify the known and unknown attacks with satisfaction results. However, apply of SVM and ELM in IDS causes a very long training time and consume high system resources, especially with large training network data. Moreover, having an adaptive IDS to detect unknown attacks in real time has become a major issue to prevent any further damage as early as possible. The current adaptive IDS solutions are either using an algorithm that learn unknown attacks or retrained both known and unknown attacks which consuming time. Therefore, in order to achieve the aims, three objectives have been identified: Firstly, to propose a MAS for IDS which consists of five agents; manager agent, coordinator agent, resident agent, analysis agent and communication agent. Secondly, to propose an intrusion detection algorithm which consists of multilevel hybrid SVM and ELM model with modified K-means for build a high-quality training datasets. Thirdly, to propose an adaptive MAS-IDS which used SVM to learn the unknown attacks and combined with the known attacks that obtained using the previous method. The experiment conducted using KDDCup’99 and NSL-KDD datasets. The experiment result shows that the proposed MAS-IDS has greatly reduced the processing time up to 81% compared without MAS. The proposed intrusion detection algorithm has also significantly improved the detection accuracy up to 95.86%, compared with the state of the art result was 95.7%. Furthermore, the proposed adaptive MAS-IDS has an ability to detect and learn unknown attacks faster up to 61% compared without MAS. It can be concluded that the adaptive MAS-IDS has a good potential for future IDS.,Certification of Master's/Doctoral Thesis" is not available	-
dc.language.iso	eng	-
dc.publisher	UKM, Bangi	-
dc.relation	Faculty of Information Science and Technology / Fakulti Teknologi dan Sains Maklumat	-
dc.rights	UKM	-
dc.subject	Multi-agent system	-
dc.subject	Adaptive real time	-
dc.subject	Intelligent agents (Computer software)	-
dc.title	Multi-agent system for an adaptive real time intrusion detection system	-
dc.type	Theses	-
dc.format.pages	245	-
dc.identifier.callno	QA76.76.I58Y347 2016 3 tesis	-
dc.identifier.barcode	002729(2017)	-
Appears in Collections:	Faculty of Information Science and Technology / Fakulti Teknologi dan Sains Maklumat

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