An efficient XGBoost–DNN-based classification model for network intrusion detection system

D Preethi; Neelu Khare

doi:10.1007/s00521-020-04708-x

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An efficient XGBoost–DNN-based classification model for network intrusion detection system

D Preethi Neelu Khare

Year: 2020 Journal: Neural Computing and Applications Vol: 32 (16)Pages: 12499-12514 Publisher: Springer Science+Business Media

DOI: 10.1007/s00521-020-04708-x

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Keywords:

Computer science Feature selection Artificial intelligence Machine learning Support vector machine Intrusion detection system Naive Bayes classifier Artificial neural network Normalization (sociology) Data mining Python (programming language) Classifier (UML) Model selection Pattern recognition (psychology)

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Network Security and Intrusion Detection

Physical Sciences → Computer Science → Computer Networks and Communications

Internet Traffic Analysis and Secure E-voting

Physical Sciences → Computer Science → Artificial Intelligence

Advanced Malware Detection Techniques

Physical Sciences → Computer Science → Signal Processing

An efficient XGBoost–DNN-based classification model for network intrusion detection system

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