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The menace of illegal access to data resources is a growing concern of researchers in the field of computer science. A significant amount of effort is required to monitor the activities in a computer network with a view to detect any attempt for intrusion. From this perspective, the main motivation behind this research is to design an efficient intrusion detection system using some novel data mining approaches that have the capability to detect intrusions with high detection rate with low false positive rate. In this work, we take multiple supports Apriori algorithm with various interestiness measures to obtain the most significant rules in detecting network intrusions. Further, we propose some novel ensemble of classifiers in order to enhance the detection rate of network attacks. Some unsupervised clustering algorithms have been proposed to further increase the detection rate of new or unseen attacks that fall under rare attacks categories. Finally, certain hybrid data mining approaches have been employed in order to design an efficient anomaly based network intrusion detection system that can achieve high detection rate and low false positive rate.
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Über die Autorin bzw. den Autor
Dr. Mrutyunjaya Panda, presently working as a Reader in PG Department of Computer Science in Utkal University, Vani Vihar, Bhubaneswar, India.He has published about 53 papers in International and national journals and conferences. He has also published 5 book chapters, 2-edited books, and 2 text books to his credit.
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A Data Mining Approach to Network Intrusion Detection
Verlag:
LAP LAMBERT Academic Publishing Feb 2015, 2015
ISBN 10: 3659633577
ISBN 13: 9783659633577
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The menace of illegal access to data resources is a growing concern of researchers in the field of computer science. A significant amount of effort is required to monitor the activities in a computer network with a view to detect any attempt for intrusion. From this perspective, the main motivation behind this research is to design an efficient intrusion detection system using some novel data mining approaches that have the capability to detect intrusions with high detection rate with low false positive rate. In this work, we take multiple supports Apriori algorithm with various interestiness measures to obtain the most significant rules in detecting network intrusions. Further, we propose some novel ensemble of classifiers in order to enhance the detection rate of network attacks. Some unsupervised clustering algorithms have been proposed to further increase the detection rate of new or unseen attacks that fall under rare attacks categories. Finally, certain hybrid data mining approaches have been employed in order to design an efficient anomaly based network intrusion detection system that can achieve high detection rate and low false positive rate. 216 pp. Englisch. Bestandsnummer des Verkäufers 9783659633577
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A Data Mining Approach to Network Intrusion Detection
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LAP LAMBERT Academic Publishing, 2015
ISBN 10: 3659633577
ISBN 13: 9783659633577
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Kartoniert / Broschiert. Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Panda MrutyunjayaDr. Mrutyunjaya Panda, presently working as a Reader in PG Department of Computer Science in Utkal University, Vani Vihar, Bhubaneswar, India.He has published about 53 papers in International and national journals an. Bestandsnummer des Verkäufers 16817672
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A Data Mining Approach to Network Intrusion Detection
Verlag:
LAP LAMBERT Academic Publishing, 2015
ISBN 10: 3659633577
ISBN 13: 9783659633577
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Taschenbuch. Zustand: Neu. A Data Mining Approach to Network Intrusion Detection | Mrutyunjaya Panda (u. a.) | Taschenbuch | 216 S. | Englisch | 2015 | LAP LAMBERT Academic Publishing | EAN 9783659633577 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Bestandsnummer des Verkäufers 104850868
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A Data Mining Approach to Network Intrusion Detection
Verlag:
LAP LAMBERT Academic Publishing Feb 2015, 2015
ISBN 10: 3659633577
ISBN 13: 9783659633577
Neu
Taschenbuch
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Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The menace of illegal access to data resources is a growing concern of researchers in the field of computer science. A significant amount of effort is required to monitor the activities in a computer network with a view to detect any attempt for intrusion. From this perspective, the main motivation behind this research is to design an efficient intrusion detection system using some novel data mining approaches that have the capability to detect intrusions with high detection rate with low false positive rate. In this work, we take multiple supports Apriori algorithm with various interestiness measures to obtain the most significant rules in detecting network intrusions. Further, we propose some novel ensemble of classifiers in order to enhance the detection rate of network attacks. Some unsupervised clustering algorithms have been proposed to further increase the detection rate of new or unseen attacks that fall under rare attacks categories. Finally, certain hybrid data mining approaches have been employed in order to design an efficient anomaly based network intrusion detection system that can achieve high detection rate and low false positive rate.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 216 pp. Englisch. Bestandsnummer des Verkäufers 9783659633577
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A Data Mining Approach to Network Intrusion Detection
Verlag:
LAP LAMBERT Academic Publishing, 2015
ISBN 10: 3659633577
ISBN 13: 9783659633577
Neu
Taschenbuch
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Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The menace of illegal access to data resources is a growing concern of researchers in the field of computer science. A significant amount of effort is required to monitor the activities in a computer network with a view to detect any attempt for intrusion. From this perspective, the main motivation behind this research is to design an efficient intrusion detection system using some novel data mining approaches that have the capability to detect intrusions with high detection rate with low false positive rate. In this work, we take multiple supports Apriori algorithm with various interestiness measures to obtain the most significant rules in detecting network intrusions. Further, we propose some novel ensemble of classifiers in order to enhance the detection rate of network attacks. Some unsupervised clustering algorithms have been proposed to further increase the detection rate of new or unseen attacks that fall under rare attacks categories. Finally, certain hybrid data mining approaches have been employed in order to design an efficient anomaly based network intrusion detection system that can achieve high detection rate and low false positive rate. Bestandsnummer des Verkäufers 9783659633577
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