Using Adaptive Neuro-Fuzzy Inference System in Alert Management of Intrusion Detection Systems

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Zahra Atashbar Orang 1,* Ezzat Moradpour 2 Ahmad Habibizad Navin 3 Amir Azimi Alasti Ahrabi 2 Mir Kamal Mirnia 4

1. Islamic Azad University, Tabriz Branch, Tabriz, Iran

2. Islamic Azad University, Shabestar Branch, Shabestar, Iran

3. Islamic Azad University, Science and Research, Tabriz, Iran

4. Islamic Azad University, Science and Research Branch, Tabriz, Iran

* Corresponding author.


Received: 4 Feb. 2012 / Revised: 16 Jun. 2012 / Accepted: 9 Aug. 2012 / Published: 8 Oct. 2012

Index Terms

Intrusion detection system, alert classification, ANFIS, false positive alert reduction


By ever increase in using computer network and internet, using Intrusion Detection Systems (IDS) has been more important. Main problems of IDS are the number of generated alerts, alert failure as well as identifying the attack type of alerts. In this paper a system is proposed that uses Adaptive Neuro-Fuzzy Inference System to classify IDS alerts reducing false positive alerts and also identifying attack types of true positive ones. By the experimental results on DARPA KDD cup 98, the system can classify alerts, leading a reduction of false positive alerts considerably and identifying attack types of alerts in low slice of time.

Cite This Paper

Zahra Atashbar Orang, Ezzat Moradpour, Ahmad Habibizad Navin, Amir Azimi Alasti Ahrabim, Mir Kamal Mirnia, "Using Adaptive Neuro-Fuzzy Inference System in Alert Management of Intrusion Detection Systems", International Journal of Computer Network and Information Security(IJCNIS), vol.4, no.11, pp.32-38, 2012. DOI:10.5815/ijcnis.2012.11.04


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