Intrusion Detection Methods Using an Ensemble of Decision Trees - Brossura

Kishor Kumar, Gulla; Pulabaigari, Viswanath; Ananda Rao, A

Brossura

ISBN 10: 6202008393 ISBN 13: 9786202008396

Casa editrice: LAP LAMBERT Academic Publishing, 2017

Riassunto

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.

Su questo libro

L'autore

Dr. 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, Nandyal, Andhra Pradesh, India. His interesting research areas are Pattern Recognition, Machine Learning and Big Data.

Dettagli dell'edizione

• Editore: LAP LAMBERT Academic Publishing
• Data di pubblicazione: 2017
• Lingua: Inglese
• ISBN 10: 6202008393
• ISBN 13: 9786202008396
• Rilegatura: Copertina flessibile
• Numero di pagine: 140
• Contatto del produttore: non disponibile
• Persona responsabile: non disponibile