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Outlier detection in stream data by machine learning and feature selection methods

In recent years, intrusion detection has emerged as an important technique for network security. Machine learning techniques have been applied to the field of intrusion detection. They can learn normal and anomalous patterns from training data and via Feature selection improving classification by se...

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Main Authors:	Koupaie, Hossein Moradi, Ibrahim, Suhaimi, Hosseinkhani, Javad
Format:	Article
Published:	2013
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://eprints.utm.my/id/eprint/40963/
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id	my.utm.40963
record_format	eprints
spelling	my.utm.409632017-08-23T03:29:40Z http://eprints.utm.my/id/eprint/40963/ Outlier detection in stream data by machine learning and feature selection methods Koupaie, Hossein Moradi Ibrahim, Suhaimi Hosseinkhani, Javad QA75 Electronic computers. Computer science In recent years, intrusion detection has emerged as an important technique for network security. Machine learning techniques have been applied to the field of intrusion detection. They can learn normal and anomalous patterns from training data and via Feature selection improving classification by searching for the subset of features which best classifies the training data to detect attacks on computer system. The quality of features directly affects the performance of classification. Many feature selection methods introduced to remove redundant and irrelevant features, because raw features may reduce accuracy or robustness of classification. Outlier detection in stream data is an important and active research issue in anomaly detection. Most of the existing outlier detection algorithms has less accurate because use some clustering method. Some data are so essential and secretary. Therefore, it needs to mine carefully even if spend cost. This paper presents a framework to detect outlier in stream data by machine learning method. Moreover, it is considered if data was high dimensional. This method is more accurate from other preferred models, because machine learning method is more accurate of other methods. 2013 Article PeerReviewed Koupaie, Hossein Moradi and Ibrahim, Suhaimi and Hosseinkhani, Javad (2013) Outlier detection in stream data by machine learning and feature selection methods. International Journal of Advanced Computer Science and Information Technology (IJACSIT), 2 (3). pp. 17-24. ISSN 2296-1739
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Koupaie, Hossein Moradi Ibrahim, Suhaimi Hosseinkhani, Javad Outlier detection in stream data by machine learning and feature selection methods
description	In recent years, intrusion detection has emerged as an important technique for network security. Machine learning techniques have been applied to the field of intrusion detection. They can learn normal and anomalous patterns from training data and via Feature selection improving classification by searching for the subset of features which best classifies the training data to detect attacks on computer system. The quality of features directly affects the performance of classification. Many feature selection methods introduced to remove redundant and irrelevant features, because raw features may reduce accuracy or robustness of classification. Outlier detection in stream data is an important and active research issue in anomaly detection. Most of the existing outlier detection algorithms has less accurate because use some clustering method. Some data are so essential and secretary. Therefore, it needs to mine carefully even if spend cost. This paper presents a framework to detect outlier in stream data by machine learning method. Moreover, it is considered if data was high dimensional. This method is more accurate from other preferred models, because machine learning method is more accurate of other methods.
format	Article
author	Koupaie, Hossein Moradi Ibrahim, Suhaimi Hosseinkhani, Javad
author_facet	Koupaie, Hossein Moradi Ibrahim, Suhaimi Hosseinkhani, Javad
author_sort	Koupaie, Hossein Moradi
title	Outlier detection in stream data by machine learning and feature selection methods
title_short	Outlier detection in stream data by machine learning and feature selection methods
title_full	Outlier detection in stream data by machine learning and feature selection methods
title_fullStr	Outlier detection in stream data by machine learning and feature selection methods
title_full_unstemmed	Outlier detection in stream data by machine learning and feature selection methods
title_sort	outlier detection in stream data by machine learning and feature selection methods
publishDate	2013
url	http://eprints.utm.my/id/eprint/40963/
_version_	1643650607041478656
score	13.211869

Outlier detection in stream data by machine learning and feature selection methods

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