Real-time intrusion detection with fuzzy genetic algorithm

Conference proceedings article

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Publication Details

Author list: Jongsuebsuk P., Wattanapongsakorn N., Charnsripinyo C.

Publisher: Hindawi

Publication year: 2013

ISBN: 9781479905454

ISSN: 0146-9428

eISSN: 1745-4557

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84883106827&doi=10.1109%2fECTICon.2013.6559603&partnerID=40&md5=06eec53a40a3032f9fae4bfe97398167

Languages: English-Great Britain (EN-GB)

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Abstract

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%. ฉ 2013 IEEE.

Keywords

Fuzzy genetic algorithm, intrusion detection, real-time detection