Improving intrusion detection for better anomaly detection based on x-means clustering and multi-layer perceptron classification

Due to excessive usage of network communication through the Internet with sensitive data in recent years, providing competent security medium to secure this data has become the most matters to be considered. One of the significant security mediums is an Intrusion Detection System (IDS) which o...

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Bibliographic Details
Main Author:	Abbas, Borkan Ahmed
Format:	Thesis
Language:	English
Published:	2016
Subjects:	Intrusion detection systems (Computer security) Anomaly detection (Computer security)
Online Access:	http://psasir.upm.edu.my/id/eprint/66741/1/FSKTM%202016%2028%20IR.pdf
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id	my-upm-ir.66741
record_format	uketd_dc
spelling	my-upm-ir.667412019-01-31T02:37:04Z Improving intrusion detection for better anomaly detection based on x-means clustering and multi-layer perceptron classification 2016-01 Abbas, Borkan Ahmed Due to excessive usage of network communication through the Internet with sensitive data in recent years, providing competent security medium to secure this data has become the most matters to be considered. One of the significant security mediums is an Intrusion Detection System (IDS) which offers anomaly detection with the proficiency to recognize unforeseen attacks. An IDSs should provide high accuracy, detection rates and low false alarm rate, but yet the majority of previous IDSs approaches suffered from the average rate of accuracy and detection as well as with high rate of false alarm .To enhance the capability of IDS, this thesis proposed a new hybrid machine learning approach based on X-Means and Multilayer perceptron called XM-MLP. X-Means used to cluster the data according to its behavior while multilayer perceptron (MLP) Neural Network classify those data into correct categories i.e. attack or normal. ISCX 2012 benchmark dataset has applied to evaluate the proposed hybrid approach against single MLP classifier and previous hybrid approaches such as KM-MLP, XM-1R and XM-NB where the core detection method is based on clustering or classification technique. The performance of the proposed hybrid approach achieves better result from a single MLP classifier and other hybrid approaches in term of accuracy, detection and false alarm rate. Intrusion detection systems (Computer security) Anomaly detection (Computer security) 2016-01 Thesis http://psasir.upm.edu.my/id/eprint/66741/ http://psasir.upm.edu.my/id/eprint/66741/1/FSKTM%202016%2028%20IR.pdf text en public masters Universiti Putra Malaysia Intrusion detection systems (Computer security) Anomaly detection (Computer security)
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English
topic	Intrusion detection systems (Computer security) Anomaly detection (Computer security)
spellingShingle	Intrusion detection systems (Computer security) Anomaly detection (Computer security) Abbas, Borkan Ahmed Improving intrusion detection for better anomaly detection based on x-means clustering and multi-layer perceptron classification
description	Due to excessive usage of network communication through the Internet with sensitive data in recent years, providing competent security medium to secure this data has become the most matters to be considered. One of the significant security mediums is an Intrusion Detection System (IDS) which offers anomaly detection with the proficiency to recognize unforeseen attacks. An IDSs should provide high accuracy, detection rates and low false alarm rate, but yet the majority of previous IDSs approaches suffered from the average rate of accuracy and detection as well as with high rate of false alarm .To enhance the capability of IDS, this thesis proposed a new hybrid machine learning approach based on X-Means and Multilayer perceptron called XM-MLP. X-Means used to cluster the data according to its behavior while multilayer perceptron (MLP) Neural Network classify those data into correct categories i.e. attack or normal. ISCX 2012 benchmark dataset has applied to evaluate the proposed hybrid approach against single MLP classifier and previous hybrid approaches such as KM-MLP, XM-1R and XM-NB where the core detection method is based on clustering or classification technique. The performance of the proposed hybrid approach achieves better result from a single MLP classifier and other hybrid approaches in term of accuracy, detection and false alarm rate.
format	Thesis
qualification_level	Master's degree
author	Abbas, Borkan Ahmed
author_facet	Abbas, Borkan Ahmed
author_sort	Abbas, Borkan Ahmed
title	Improving intrusion detection for better anomaly detection based on x-means clustering and multi-layer perceptron classification
title_short	Improving intrusion detection for better anomaly detection based on x-means clustering and multi-layer perceptron classification
title_full	Improving intrusion detection for better anomaly detection based on x-means clustering and multi-layer perceptron classification
title_fullStr	Improving intrusion detection for better anomaly detection based on x-means clustering and multi-layer perceptron classification
title_full_unstemmed	Improving intrusion detection for better anomaly detection based on x-means clustering and multi-layer perceptron classification
title_sort	improving intrusion detection for better anomaly detection based on x-means clustering and multi-layer perceptron classification
granting_institution	Universiti Putra Malaysia
publishDate	2016
url	http://psasir.upm.edu.my/id/eprint/66741/1/FSKTM%202016%2028%20IR.pdf
_version_	1747812400851058688

Improving intrusion detection for better anomaly detection based on x-means clustering and multi-layer perceptron classification

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