A comparison of different technique in flow based anomaly detection

By performing network traffic analyzing in different datasets, Intrusion Detection Systems (IDS) that works based on anomaly techniques learn the pattern of anomalous and normal behavior. The huge data size in IDSs dataset to process is known as the trend challenge. It causes high false alarms rates...

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Bibliographic Details
Main Author:	Salah, Mohammad
Format:	Thesis
Language:	English
Published:	2017
Subjects:	Anomaly detection (Computer security) Intrusion detection systems (Computer security)
Online Access:	http://psasir.upm.edu.my/id/eprint/71066/1/FSKTM%202017%2010%20-%20IR.pdf
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id	my-upm-ir.71066
record_format	uketd_dc
spelling	my-upm-ir.710662019-08-13T08:37:06Z A comparison of different technique in flow based anomaly detection 2017-01 Salah, Mohammad By performing network traffic analyzing in different datasets, Intrusion Detection Systems (IDS) that works based on anomaly techniques learn the pattern of anomalous and normal behavior. The huge data size in IDSs dataset to process is known as the trend challenge. It causes high false alarms rates and low rates of detection. In this proposal, a new method which functions based on the Online Sequential Extreme Learning Machine (OS-ELM) is introduced for detecting intrusions in the network. Our proposed method detect anomaly by using alpha profiling technique and by utilizing a group of filtered, feature selection techniques based on Consistency and Correlation has eliminated the inappropriate features. Beta profiling technique has been used in order to decrease the training dataset’s size, as an alternative for sampling technique. In order to evaluate the efficiency of the proposed method we used the standard version of Network Security Laboratory-Knowledge Discovery and Data Mining (NSL-KDD2009) dataset. According to the primary achieved results from our experiments, it is assumed that our proposed IDS method can achieve lower rate of false positive and higher accuracy when using NSL-KDD dataset. It can also be seen that our proposed method is more eƥcient than conventional methods in intrusion detection. Anomaly detection (Computer security) Intrusion detection systems (Computer security) 2017-01 Thesis http://psasir.upm.edu.my/id/eprint/71066/ http://psasir.upm.edu.my/id/eprint/71066/1/FSKTM%202017%2010%20-%20IR.pdf text en public masters Universiti Putra Malaysia Anomaly detection (Computer security) Intrusion detection systems (Computer security)
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English
topic	Anomaly detection (Computer security) Intrusion detection systems (Computer security)
spellingShingle	Anomaly detection (Computer security) Intrusion detection systems (Computer security) Salah, Mohammad A comparison of different technique in flow based anomaly detection
description	By performing network traffic analyzing in different datasets, Intrusion Detection Systems (IDS) that works based on anomaly techniques learn the pattern of anomalous and normal behavior. The huge data size in IDSs dataset to process is known as the trend challenge. It causes high false alarms rates and low rates of detection. In this proposal, a new method which functions based on the Online Sequential Extreme Learning Machine (OS-ELM) is introduced for detecting intrusions in the network. Our proposed method detect anomaly by using alpha profiling technique and by utilizing a group of filtered, feature selection techniques based on Consistency and Correlation has eliminated the inappropriate features. Beta profiling technique has been used in order to decrease the training dataset’s size, as an alternative for sampling technique. In order to evaluate the efficiency of the proposed method we used the standard version of Network Security Laboratory-Knowledge Discovery and Data Mining (NSL-KDD2009) dataset. According to the primary achieved results from our experiments, it is assumed that our proposed IDS method can achieve lower rate of false positive and higher accuracy when using NSL-KDD dataset. It can also be seen that our proposed method is more eƥcient than conventional methods in intrusion detection.
format	Thesis
qualification_level	Master's degree
author	Salah, Mohammad
author_facet	Salah, Mohammad
author_sort	Salah, Mohammad
title	A comparison of different technique in flow based anomaly detection
title_short	A comparison of different technique in flow based anomaly detection
title_full	A comparison of different technique in flow based anomaly detection
title_fullStr	A comparison of different technique in flow based anomaly detection
title_full_unstemmed	A comparison of different technique in flow based anomaly detection
title_sort	comparison of different technique in flow based anomaly detection
granting_institution	Universiti Putra Malaysia
publishDate	2017
url	http://psasir.upm.edu.my/id/eprint/71066/1/FSKTM%202017%2010%20-%20IR.pdf
_version_	1747812964596973568

A comparison of different technique in flow based anomaly detection

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