gulla kishor kumar (6 Ergebnisse)

Paperback. Zustand: Brand New. 140 pages. 8.66x5.91x0.32 inches. In Stock.

Taschenbuch. Zustand: Neu. Intrusion Detection Methods Using an Ensemble of Decision Trees | Gulla Kishor Kumar (u. a.) | Taschenbuch | 140 S. | Englisch | 2017 | LAP LAMBERT Academic Publishing | EAN 9786202008396 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Intrusion detection corresponds to a set of techniques that are used to find attacks which damages the computers and network infrastructures. Intrusion detection is a classification problem. Therefore, data mining techniques can be used to classify a given network connection to either a normal connection or an anomaly connection. To do this, various classification models can be used. Among all, decision tree classifiers have become very popular because of its simplicity, interpretability and its performance. However, decision tree classifiers are known to have high variance. Therefore, it is said to be an unstable classifier. Along with these, the conventional decision tree classifier does not perform well when noise, vagueness and uncertainty present in the data. However, to resolve the above issues this book proposes to use an ensemble of decision tree classifiers. To show the effectiveness of the proposed methods, various intrusion detection data sets along with standard data sets are used. 140 pp. Englisch.

Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Kishor Kumar GullaDr. G.Kishor Kumar recieved M.Tech and Ph.D from JNTUA, Ananthapuramu. He is working as Professor and Head of the Department of Information Technology at Rajeev Gandhi Memorial College of Engineering and Technology,.

Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Intrusion detection corresponds to a set of techniques that are used to find attacks which damages the computers and network infrastructures. Intrusion detection is a classification problem. Therefore, data mining techniques can be used to classify a given network connection to either a normal connection or an anomaly connection. To do this, various classification models can be used. Among all, decision tree classifiers have become very popular because of its simplicity, interpretability and its performance. However, decision tree classifiers are known to have high variance. Therefore, it is said to be an unstable classifier. Along with these, the conventional decision tree classifier does not perform well when noise, vagueness and uncertainty present in the data. However, to resolve the above issues this book proposes to use an ensemble of decision tree classifiers. To show the effectiveness of the proposed methods, various intrusion detection data sets along with standard data sets are used.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 140 pp. Englisch.

Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Intrusion detection corresponds to a set of techniques that are used to find attacks which damages the computers and network infrastructures. Intrusion detection is a classification problem. Therefore, data mining techniques can be used to classify a given network connection to either a normal connection or an anomaly connection. To do this, various classification models can be used. Among all, decision tree classifiers have become very popular because of its simplicity, interpretability and its performance. However, decision tree classifiers are known to have high variance. Therefore, it is said to be an unstable classifier. Along with these, the conventional decision tree classifier does not perform well when noise, vagueness and uncertainty present in the data. However, to resolve the above issues this book proposes to use an ensemble of decision tree classifiers. To show the effectiveness of the proposed methods, various intrusion detection data sets along with standard data sets are used.