APPLICATION OF ARTIFICIAL NEURAL NETWORKS IN THE INTRUSION DETECTION SYSTEM

被引：0
|
作者：
Mustafaev, Arslan G. [1 ]
机构：
[1] Dagestan State Univ Natl Econ, Dept Informat Technol & Informat Secur, Makhachkala, Russia
来源：
INTERNATIONAL JOURNAL ON INFORMATION TECHNOLOGIES AND SECURITY | 2018年 / 10卷 / 04期
关键词：
Intrusion detection system; adaptability; classification; artificial neural networks; analysis of network traffic; computer networks;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Intrusion detection systems classify network traffic into two main categories: normal activity and the actions of an attacker. Currently, intelligent data processing and machine learning play an important role in many areas of activity, not excluding intrusion detection systems. One of the main steps in data mining is the identification of an optimal data set that helps to improve the efficiency, performance and speed of predicting intrusion detection systems. For the experimental analysis, a NSL-KDD database used. The results of the experiments show that the approach proposed in the paper is accurate enough, with a low number of false positives and high sensitivity, requiring less training time than using a complete set of data.
引用
收藏
页码：57 / 66
页数：10
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