9783847373162 - detection and classification of normal and anomaly ip packet: network intrusion detection through neural network hybrid learning with data transformation analysis di munawar, saima (5 risultati)
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Da: preigu, Osnabrück, Germaniapreigu
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Taschenbuch. Condizione: Neu. Detection and Classification Of Normal and Anomaly IP Packet | Network intrusion detection through Neural Network Hybrid Learning with Data Transformation Analysis | Saima Munawar | Taschenbuch | 108 S. | Englisch | 2012 | LAP LAMBERT Academic Publishing | EAN 9783847373162 | Verantwortliche Person…für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.
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Da: Mispah books, Redhill, SURRE, Regno UnitoMispah books
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paperback. Condizione: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Intrusion detection system is a vital part of computer security system commonly used for precaution and detection. It is built for classifier or descriptive or predictive model to proficient classification of normal behavior from a…bnormal behavior of IP packets. This book presents the solution regarding proper data transformation methods handling and importance of data analysis of complete data set which is apply on hybrid neural network approaches for used to cluster and classify normal and abnormal behavior to improve the accuracy of network based anomaly detection classifier. Because neural network classes only require the numerical form of data but IP connections or packets of network have some symbolic features which are difficult to handle without the proper data transformation analysis. For this reason, it got non redundant new NSL KDD CUP data set. The experimental results show that indicator variable is more effective as compared to the both conditional probabilities and arbitrary assignment method from measurement of accuracy and balance error rate. 108 pp. Englisch.
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Intrusion detection system is a vital part of computer security system commonly used for precaution and detection. It is built for classifier or descriptive or predictive model to proficient classification of normal behavior from abnor…mal behavior of IP packets. This book presents the solution regarding proper data transformation methods handling and importance of data analysis of complete data set which is apply on hybrid neural network approaches for used to cluster and classify normal and abnormal behavior to improve the accuracy of network based anomaly detection classifier. Because neural network classes only require the numerical form of data but IP connections or packets of network have some symbolic features which are difficult to handle without the proper data transformation analysis. For this reason, it got non redundant new NSL KDD CUP data set. The experimental results show that indicator variable is more effective as compared to the both conditional probabilities and arbitrary assignment method from measurement of accuracy and balance error rate.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 108 pp. Englisch.
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Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
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EUR 49,59
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Intrusion detection system is a vital part of computer security system commonly used for precaution and detection. It is built for classifier or descriptive or predictive model to proficient classification of normal behavior from abnorm…al behavior of IP packets. This book presents the solution regarding proper data transformation methods handling and importance of data analysis of complete data set which is apply on hybrid neural network approaches for used to cluster and classify normal and abnormal behavior to improve the accuracy of network based anomaly detection classifier. Because neural network classes only require the numerical form of data but IP connections or packets of network have some symbolic features which are difficult to handle without the proper data transformation analysis. For this reason, it got non redundant new NSL KDD CUP data set. The experimental results show that indicator variable is more effective as compared to the both conditional probabilities and arbitrary assignment method from measurement of accuracy and balance error rate.
