Incoherent Dictionary Learning for Sparse Representation in Network Anomaly Detection
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RIS BIB ENDNOTEIncoherent Dictionary Learning for Sparse Representation in Network Anomaly Detection
Publication date: 11.04.2016
Schedae Informaticae, 2015, Volume 24, pp. 63-71
https://doi.org/10.4467/20838476SI.15.006.3028Authors
Incoherent Dictionary Learning for Sparse Representation in Network Anomaly Detection
In this article we present the use of sparse representation of a signal and incoherent dictionary learning method for the purpose of network traffic analysis. In learning process we use 1D INK-SVD algorithm to detect proper dictionary structure. Anomaly detection is realized by parameter estimation of the analyzed signal and its comparative analysis to network traffic profiles. Efficiency of our method is examined with the use of extended set of test traces from real network traffic. Received experimental results confirm effectiveness of the presented method.
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Information: Schedae Informaticae, 2015, Volume 24, pp. 63-71
Article type: Original article
UTP University of Science and Technology Institute of Telecommunications
UTP University of Science and Technology Institute of Telecommunications
Published at: 11.04.2016
Article status: Open
Licence: None
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