Network intrusion detection with Fuzzy Genetic Algorithm for unknown attacks

Conference proceedings article

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Publication Details

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

Publication year: 2013

Start page: 1

End page: 5

Number of pages: 5

ISBN: 9781467357401

ISSN: 1976-7684

eISSN: 1976-7684

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84876768939&doi=10.1109%2fICOIN.2013.6496342&partnerID=40&md5=c41dbec60f7339f7fe72c6966eba7437

Languages: English-Great Britain (EN-GB)

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Abstract

In this work, we consider detecting unknown or new network attack types with a Fuzzy Genetic Algorithm approach. The fuzzy rule is a supervised learning technique and genetic algorithm make fuzzy rule able to learn new attacks by itself. Moreover, this technique has high detection rate and robust. Therefore, we apply the fuzzy genetic algorithm approach to our real-time intrusion detection system implementation i.e. the data is detected right after it arrived to the detection system. In our experiments, various denial of service (DoS) attacks and Probe attacks are considered. We evaluate our IDS in terms of detection time, detection rate and false alarm rate. From the experiment, we obtain the average detection rate approximately over 97%. ฉ 2013 IEEE.

Keywords

IDS, network intrustion detection, unknown detection