Feature Selection For Intrusion Detection Systems: Using data mining techniques - Softcover

Kumar, Yogesh; Kumar, Krishan; Kumar, Gulshan

9783659515101: Feature Selection For Intrusion Detection Systems: Using data mining techniques

Softcover

ISBN 10: 3659515108 ISBN 13: 9783659515101

Verlag: LAP LAMBERT Academic Publishing, 2014

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Network security is a serious global concern. The increasing prevalence of malware and incidents of attacks hinders the utilization of the Internet to its greatest benefit and incur significant economic losses. The traditional approaches in securing systems against threats are designing mechanisms that create a protective shield, almost always with vulnerabilities. This has created Intrusion Detection Systems to be developed that complement traditional approaches. However, with the advancement of computer technology, the behavior of intrusions has become complex that makes the work of security experts hard to analyze and detect intrusions. In order to address these challenges, data mining techniques have become a possible solution. However, the performance of data mining algorithms is affected when no optimized features are provided. This is because, complex relationships can be seen as well between the features and intrusion classes contributing to high computational costs in processing tasks, subsequently leads to delays in identifying intrusions. Feature selection is thus important in detecting intrusions by allowing the data mining system to focus on what is really important.

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Biograf�a del autor

Yogesh Kumar has done M Tech (CSE) from PIT (PTU Main campus), Kapurthala, India. Currently, he is an Assistant Professor at BGIET, Sangrur, India. His general research Interests are in the areas of Information Security and Computer Networks.

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VerlagLAP LAMBERT Academic Publishing
Erscheinungsdatum2014
ISBN 10 3659515108
ISBN 13 9783659515101
EinbandTapa blanda
SpracheEnglisch
Anzahl der Seiten100

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Feature Selection For Intrusion Detection Systems

Yogesh Kumar

Verlag: LAP LAMBERT Academic Publishing Jan 2014, 2014

ISBN 10: 3659515108 ISBN 13: 9783659515101

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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Network security is a serious global concern. The increasing prevalence of malware and incidents of attacks hinders the utilization of the Internet to its greatest benefit and incur significant economic losses. The traditional approaches in securing systems against threats are designing mechanisms that create a protective shield, almost always with vulnerabilities. This has created Intrusion Detection Systems to be developed that complement traditional approaches. However, with the advancement of computer technology, the behavior of intrusions has become complex that makes the work of security experts hard to analyze and detect intrusions. In order to address these challenges, data mining techniques have become a possible solution. However, the performance of data mining algorithms is affected when no optimized features are provided. This is because, complex relationships can be seen as well between the features and intrusion classes contributing to high computational costs in processing tasks, subsequently leads to delays in identifying intrusions. Feature selection is thus important in detecting intrusions by allowing the data mining system to focus on what is really important. 100 pp. Englisch. Bestandsnummer des Verk�ufers 9783659515101

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Feature Selection For Intrusion Detection Systems : Using data mining techniques

Yogesh Kumar

Verlag: LAP LAMBERT Academic Publishing, 2014

ISBN 10: 3659515108 ISBN 13: 9783659515101

Neu Taschenbuch

Print-on-Demand

Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland

Verk�uferbewertung 5 von 5 Sternen

Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Network security is a serious global concern. The increasing prevalence of malware and incidents of attacks hinders the utilization of the Internet to its greatest benefit and incur significant economic losses. The traditional approaches in securing systems against threats are designing mechanisms that create a protective shield, almost always with vulnerabilities. This has created Intrusion Detection Systems to be developed that complement traditional approaches. However, with the advancement of computer technology, the behavior of intrusions has become complex that makes the work of security experts hard to analyze and detect intrusions. In order to address these challenges, data mining techniques have become a possible solution. However, the performance of data mining algorithms is affected when no optimized features are provided. This is because, complex relationships can be seen as well between the features and intrusion classes contributing to high computational costs in processing tasks, subsequently leads to delays in identifying intrusions. Feature selection is thus important in detecting intrusions by allowing the data mining system to focus on what is really important. Bestandsnummer des Verk�ufers 9783659515101

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