Data Mining for Network Intrusion Detection: A Comparison of Alternative Methods

Intrusion detection systems help network administrators prepare for and deal with network security attacks. These systems collect information from a variety of systems and network sources, and analyze them for signs of intrusion and misuse. A variety of techniques have been employed for analysis ran...

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Main Authors: ZHU, Dan, Premkumar, G., ZHANG, Xiaoning, CHU, Chao-Hsien
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2007
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Online Access:https://ink.library.smu.edu.sg/sis_research/1764
http://onlinelibrary.wiley.com/doi/10.1111/j.1540-5915.2001.tb00975.x/abstract
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spelling sg-smu-ink.sis_research-27632013-03-15T10:12:03Z Data Mining for Network Intrusion Detection: A Comparison of Alternative Methods ZHU, Dan Premkumar, G. ZHANG, Xiaoning CHU, Chao-Hsien Intrusion detection systems help network administrators prepare for and deal with network security attacks. These systems collect information from a variety of systems and network sources, and analyze them for signs of intrusion and misuse. A variety of techniques have been employed for analysis ranging from traditional statistical methods to new data mining approaches. In this study the performance of three data mining methods in detecting network intrusion is examined. An experimental design is created to evaluate the impact of three data mining methods, two data representation formats, and two data proportion schemes on the classification accuracy of intrusion detection systems. The results indicate that data mining methods and data proportion have a significant impact on classification accuracy. Within data mining methods, rough sets provide better accuracy, followed by neural networks and inductive learning. Balanced data proportion performs better than unbalanced data proportion. There are no major differences in performance between binary and integer data representation. 2007-06-01T07:00:00Z text https://ink.library.smu.edu.sg/sis_research/1764 info:doi/10.1111/j.1540-5915.2001.tb00975.x http://onlinelibrary.wiley.com/doi/10.1111/j.1540-5915.2001.tb00975.x/abstract Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Data Mining Inductive Learning Intrusion Detection Network Security Neural Networks Rough Sets and Telecommunications Computer Sciences Management Information Systems Numerical Analysis and Scientific Computing
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Data Mining
Inductive Learning
Intrusion Detection
Network Security
Neural Networks
Rough Sets
and Telecommunications
Computer Sciences
Management Information Systems
Numerical Analysis and Scientific Computing
spellingShingle Data Mining
Inductive Learning
Intrusion Detection
Network Security
Neural Networks
Rough Sets
and Telecommunications
Computer Sciences
Management Information Systems
Numerical Analysis and Scientific Computing
ZHU, Dan
Premkumar, G.
ZHANG, Xiaoning
CHU, Chao-Hsien
Data Mining for Network Intrusion Detection: A Comparison of Alternative Methods
description Intrusion detection systems help network administrators prepare for and deal with network security attacks. These systems collect information from a variety of systems and network sources, and analyze them for signs of intrusion and misuse. A variety of techniques have been employed for analysis ranging from traditional statistical methods to new data mining approaches. In this study the performance of three data mining methods in detecting network intrusion is examined. An experimental design is created to evaluate the impact of three data mining methods, two data representation formats, and two data proportion schemes on the classification accuracy of intrusion detection systems. The results indicate that data mining methods and data proportion have a significant impact on classification accuracy. Within data mining methods, rough sets provide better accuracy, followed by neural networks and inductive learning. Balanced data proportion performs better than unbalanced data proportion. There are no major differences in performance between binary and integer data representation.
format text
author ZHU, Dan
Premkumar, G.
ZHANG, Xiaoning
CHU, Chao-Hsien
author_facet ZHU, Dan
Premkumar, G.
ZHANG, Xiaoning
CHU, Chao-Hsien
author_sort ZHU, Dan
title Data Mining for Network Intrusion Detection: A Comparison of Alternative Methods
title_short Data Mining for Network Intrusion Detection: A Comparison of Alternative Methods
title_full Data Mining for Network Intrusion Detection: A Comparison of Alternative Methods
title_fullStr Data Mining for Network Intrusion Detection: A Comparison of Alternative Methods
title_full_unstemmed Data Mining for Network Intrusion Detection: A Comparison of Alternative Methods
title_sort data mining for network intrusion detection: a comparison of alternative methods
publisher Institutional Knowledge at Singapore Management University
publishDate 2007
url https://ink.library.smu.edu.sg/sis_research/1764
http://onlinelibrary.wiley.com/doi/10.1111/j.1540-5915.2001.tb00975.x/abstract
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