Applications of neural networks in network intrusion detection

被引：0

作者：

Lazarevic, Aleksandar ^{[1
]}

Pokrajac, Dragoljub ^{[2
]}

Nikolic, Jelena ^{[3
]}

机构：

[1] United Technol Res Ctr, 411 Silver Lane, E Hartford, CT 06108 USA

[2] Delaware State Univ, Appl Math Res Ctr, Dover 19904, DE USA

[3] Univ Nis, Fac Elect Engn, Nish 18000, Serbia

来源：

NEUREL 2006: EIGHT SEMINAR ON NEURAL NETWORK APPLICATIONS IN ELECTRICAL ENGINEERING, PROCEEDINGS | 2006年

基金：

美国国家科学基金会;

关键词：

neural networks; rare class; network intrusion detection;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：59 / +

页数：3

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