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A fuzzy anomaly detection system based on hybrid PSO-Kmeans algorithm in content-centric networks

Author
Karami, A.; Guerrero, M.
Type of activity
Journal article
Journal
Neurocomputing
Date of publication
2015-02-03
Volume
149, Part C
First page
1253
Last page
1269
DOI
https://doi.org/10.1016/j.neucom.2014.08.070 Open in new window
Repository
http://hdl.handle.net/2117/28322 Open in new window
URL
http://www.sciencedirect.com/science/article/pii/S0925231214011588 Open in new window
Abstract
In Content-Centric Networks (CCNs) as a possible future Internet, new kinds of attacks and security challenges – from Denial of Service (DoS) to privacy attacks – will arise. An efficient and effective security mechanism is required to secure content and defense against unknown and new forms of attacks and anomalies. Usually, clustering algorithms would fit the requirements for building a good anomaly detection system. K-means is a popular anomaly detection method to classify data into diffe...
Citation
Karami, A.; Guerrero, M. A fuzzy anomaly detection system based on hybrid PSO-Kmeans algorithm in content-centric networks. "Neurocomputing", 03 Febrer 2015, vol. 149, Part C, p. 1253-1269.
Keywords
Anomaly detection, Clustering analysis, Content-centric networks, Fuzzy set, Particle swarm optimization K-means
Group of research
CNDS - Computer Networks and Distributed Systems

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