Fuzzy Clustering Based Approach to Network Traffic Classification and Anomaly Detection

DATA ANALYTICS 2015 : The Fourth International Conference on Data Analytics 2015
Jūlija Asmuss, Gunārs Lauks

We develop network traffic classification and anomaly detection methods based on traffic time series analysis using fuzzy clustering. We compare four fuzzy clustering techniques using different dimensionality reduction methods and validity indices to work out effective anomaly detection algorithm. Effectiveness of the proposed classification system is evaluated on traffic data with and without traffic attack components.

Keywords
fuzzy clustering; fuzzy transform; traffic classification; anomaly detection
Hyperlink
https://www.thinkmind.org/index.php?view=article&articleid=data_analytics_2015_5_10_60138

Asmuss, J., Lauks, G. Fuzzy Clustering Based Approach to Network Traffic Classification and Anomaly Detection. In: DATA ANALYTICS 2015 : The Fourth International Conference on Data Analytics, France, Nice, 19-24 July, 2015. Nice: IARIA, 2015, pp.78-80. ISBN 978-1-61208-423-7. ISSN 2308-4464.

Publication language
English (en)