Applications of Neural Networks in Network Intrusion Detection

Author

Lazarevic, Aleksandar ; Pokrajac, Dragoijub ; Nikolic, Jelena

Author_Institution

United Technol. Res. Center, East Hartford, CT

fYear

2006

fDate

25-27 Sept. 2006

Firstpage

59

Lastpage

64

Abstract

In this paper, we discuss the applications of multilayer perceptrons for classification of network intrusion detection data characterized by skewed class distributions. We compare several methods for learning from such skewed distributions by manipulating data records. The investigated methods include oversampling, undersampling and generating artificial data records using SMOTE technique. The presented methods are tested on KDDCup99 network intrusion dataset and compared using various classification performance metrics. In addition, the influence of decision margin on recall and misclassification rates is also examined

Keywords

computer networks; learning (artificial intelligence); multilayer perceptrons; security of data; KDDCup99 network intrusion dataset; SMOTE technique; classification performance metrics; data records manipulation; multilayer perceptrons; network intrusion detection; neural networks; skewed class distribution; Data mining; Intrusion detection; Measurement; Multi-layer neural network; Multilayer perceptrons; Neural networks; Predictive models; Seminars; Testing; World Wide Web; Neural networks; network intrusion detection; rare class;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Network Applications in Electrical Engineering, 2006. NEUREL 2006. 8th Seminar on

Conference_Location

Belgrade, Serbia & Montenegro

Print_ISBN

1-4244-0433-9

Electronic_ISBN

1-4244-0433-9

Type

conf

DOI

10.1109/NEUREL.2006.341176

Filename

4147164