Real-Time Intrusion Detection with Fuzzy Genetic Algorithm

被引：0
|
作者：
Jongsuebsuk, P. [1 ]
Wattanapongsakorn, N. [1 ]
Charnsripinyo, C. [2 ]
机构：
[1] King Mongkuts Univ Technol Thonburi, Dept Comp Engn, Bangkok, Thailand
[2] Natl Elect & Comp Technol Ctr, ] Pathum Thani, Thailand
来源：
2013 10TH INTERNATIONAL CONFERENCE ON ELECTRICAL ENGINEERING/ELECTRONICS, COMPUTER, TELECOMMUNICATIONS AND INFORMATION TECHNOLOGY (ECTI-CON) | 2013年
关键词：
Fuzzy genetic algorithm; intrusion detection; real-time detection; network security;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
In this work, we consider network intrusion detection using fuzzy genetic algorithm to classify network attack data. Fuzzy rule is a machine learning algorithm that can classify network attack data, while a genetic algorithm is an optimization algorithm that can help finding appropriate fuzzy rule and give the best/optimal solution. In this paper, we consider both well-known KDD99 dataset and our own network dataset. The KDD99 dataset is a benchmark dataset that is used in various researches while our network dataset is an online network data captured in actual network environment. We evaluate our IDS in terms of detection speed, detection rate and false alarm rate. From the experiment, we can detect network attack in real-time (or within 2-3 seconds) after the data arrives at the detection system. The detection rate of our algorithm is approximately over 97.5%
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页数：6
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