Network traffic classification for anomaly detection fuzzy clustering based approach

Jūlija Asmuss; G. Lauks

doi:10.1109/fskd.2015.7381960

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Network traffic classification for anomaly detection fuzzy clustering based approach

Jūlija Asmuss G. Lauks

Year: 2015 Pages: 313-318

DOI: 10.1109/fskd.2015.7381960

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Abstract

In this paper we develop network traffic classification and anomaly detection methods based on traffic time series analysis using fuzzy clustering technique. The effectiveness of fuzzy and possibilistic algorithms is compared on generated traffic data with and without traffic attack components.

Keywords:

Anomaly detection Computer science Data mining Cluster analysis Anomaly (physics) Fuzzy logic Fuzzy clustering Artificial intelligence Pattern recognition (psychology)

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Network Security and Intrusion Detection

Physical Sciences → Computer Science → Computer Networks and Communications

Internet Traffic Analysis and Secure E-voting

Physical Sciences → Computer Science → Artificial Intelligence

Spam and Phishing Detection

Physical Sciences → Computer Science → Information Systems

Network traffic classification for anomaly detection fuzzy clustering based approach

Abstract

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