An efficient distributed learning-based model for network intrusion detection system

Aryan Mohammadi Pasikhani (P75855)

Please use this identifier to cite or link to this item: https://ptsldigital.ukm.my/jspui/handle/123456789/476443

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DC Field	Value	Language
dc.contributor.advisor	Elankovan A Sundararajan, Assoc. Prof. Dr.
dc.contributor.author	Aryan Mohammadi Pasikhani (P75855)
dc.date.accessioned	2023-10-06T09:18:34Z	-
dc.date.available	2023-10-06T09:18:34Z	-
dc.date.issued	2017-02-20
dc.identifier.other	ukmvital:86188
dc.identifier.uri	https://ptsldigital.ukm.my/jspui/handle/123456789/476443	-
dc.description	The large increase in computer network usage, and the huge amount of sensitive data being stored and transferred through them, has escalated the attacks and invasions on these networks. The system that monitors the events occurring in a computer system or a network and analyzes the events for signs of intrusion is known as an Intrusion Detection System (IDS). In information protection, the Intrusion Detection System (IDS) has become a crucial component in terms of computer and network security which monitors the network traffic to detect possible security threats. It is used to safeguard data confidentiality, integrity and system availability from various types of attacks. There are various approaches being utilized in intrusion detections, but unfortunately none of the systems so far are not completely flawless and suffer from a number of drawbacks such as: Low accuracy to detect new types of attacks and misclassification of normal traffic, known as false positive and misclassification of malicious traffic, known as false negative alarms, in addition to long response time. It is necessary to develop an IDS that is accurate, adaptive, and extensible to overcome these weaknesses. In this research, propose a new learning-based method which improves IDS adaptability to new attacks and reduces false alarms. The method has a distributed architecture to increase performance and scalability of the IDS and uses C4.5 decision trees with the feedback learning technique to adapt dynamic network behaviors. To evaluate the proposed method, we used some well-known datasets in this context such as KDD Cup 99 and did several tests with approximately over 90 percent detection accuracy on benchmarks. According to the promising results, the adaptable IDS approach is more accurate than traditional systems and it is more efficient against new complex network attacks.,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	Network
dc.subject	Intrusion Detection System
dc.subject	Low accuracy
dc.subject	Learning-based model
dc.subject	Universiti Kebangsaan Malaysia -- Dissertations
dc.title	An efficient distributed learning-based model for network intrusion detection system
dc.type	theses
dc.format.pages	100
dc.identifier.barcode	002684(2017)
Appears in Collections:	Faculty of Information Science and Technology / Fakulti Teknologi dan Sains Maklumat

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