Automated network feature weighting-based anomaly detection

Research output: A Conference proceeding or a Chapter in BookConference contribution

2 Citations (Scopus)

Abstract

We propose in this paper an automated feature weighting method based on fuzzy subspace approach to assign a weight to each network feature depending on its degree of importance in anomaly detection. Fuzzy c-means and fuzzy entropy modeling are used to calculate weight values and k-means vector quantization is used to model network patterns. The proposed method not only increases the detection rate but also reduces false alarm rate as shown in our experiments.

Original languageEnglish
Title of host publication2008 IEEE International Conference on Intelligence and Security Informatics
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages162-166
Number of pages5
DOIs
Publication statusPublished - 2008
EventIEEE International Conference on Intelligence and Security Informatics, 2008, IEEE ISI 2008 - Taipei, Taiwan, Province of China
Duration: 17 Jun 200820 Jun 2008

Conference

ConferenceIEEE International Conference on Intelligence and Security Informatics, 2008, IEEE ISI 2008
CountryTaiwan, Province of China
CityTaipei
Period17/06/0820/06/08

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Tran, D., Ma, W., & Sharma, D. (2008). Automated network feature weighting-based anomaly detection. In 2008 IEEE International Conference on Intelligence and Security Informatics (pp. 162-166). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ISI.2008.4565047