Comparative Study of Machine Learning Algorithm for Intrusion Detection System

被引：7

作者：

Sravani, K. ^{[1
]}

Srinivasu, P. ^{[1
]}

机构：

[1] Anil Neerukonda Inst Technol & Sci, Dept Comp Sci & Engn, Visakhapatnam, Andhra Pradesh, India

来源：

PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON FRONTIERS OF INTELLIGENT COMPUTING: THEORY AND APPLICATIONS (FICTA) 2013 | 2014年 / 247卷

关键词：

IDS; Machine learning algorithms; KDDCUP99; DATASET; Confusion Matrix;

D O I：

10.1007/978-3-319-02931-3_23

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Now a day's, Intrusion detection is a very important research area in network security. Machine learning techniques have been applied to the field of intrusion detection. In this paper, we use KDD Cup 99' data set for taking samples. For these samples we use classification algorithms to classify the network traffic data. In this paper, we are going to compare our results with features selected using Naive Bayes, Neural Networks. We are trying to use standard measurements like detection rate, false positive, false negative, accuracy and Confusion Matrix.

引用

页码：189 / 196

页数：8

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