Hybrid fuzzy adaptive wiener filtering with optimization for intrusion detection

8Citations
Citations of this article
13Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

Intrusion detection plays a key role in detecting attacks over networks, and due to the increasing usage of Internet services, several security threats arise. Though an intrusion detection system (IDS) detects attacks efficiently, it also generates a large number of false alerts, which makes it difficult for a system administrator to identify attacks. This paper proposes automatic fuzzy rule generation combined with a Wiener filter to identify attacks. Further, to optimize the results, simplified swarm optimization is used. After training a large dataset, various fuzzy rules are generated automatically for testing, and a Wiener filter is used to filter out attacks that act as noisy data, which improves the accuracy of the detection. By combining automatic fuzzy rule generation with a Wiener filter, an IDS can handle intrusion detection more efficiently. Experimental results, which are based on collected live network data, are discussed and show that the proposed method provides a competitively high detection rate and a reduced false alarm rate in comparison with other existing machine learning techniques.

Cite

CITATION STYLE

APA

Sujendran, R., & Arunachalam, M. (2015). Hybrid fuzzy adaptive wiener filtering with optimization for intrusion detection. ETRI Journal, 37(3), 502–511. https://doi.org/10.4218/etrij.15.0114.0275

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free