Estimation of distribution algorithm for optimization of neural networks for intrusion detection system

被引：0
|
作者：
Chen, Yuehui [1 ]
Zhang, Yong
Abraham, Ajith
机构：
[1] Jinan Univ, Sch Informat Sci & Engn, Jinan 250022, Peoples R China
[2] Jinan Univ, Sch Control Sci & Engn, Jinan 250022, Peoples R China
[3] Chung Ang Univ, Sch Comp Sci & Engn, Seoul 156756, South Korea
来源：
ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING - ICAISC 2006, PROCEEDINGS | 2006年 / 4029卷
关键词：
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：
An Intrusion Detection System (IDS) is a program that analyzes what happens or has happened during an execution and tries to find indications that the computer has been misused. An IDS does not eliminate the use of preventive mechanism but it works as the last defensive mechanism in securing the system. This paper evaluates the performances of Estimation of Distribution Algorithm (EDA) to train a feed-forward neural network classifier for detecting intrusions in a network. Results are then compared with Particle Swarm Optimization (PSO) based neural classifier and Decision Trees (DT). Empirical results clearly show that evolutionary computing techniques could play an important role in designing real time intrusion detection systems.
引用
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页码：9 / 18
页数：10
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