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2015 | 24 |
Article title

Incoherent Dictionary Learning for Sparse Representation in Network Anomaly Detection

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PL
Abstracts
PL
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.
Publisher
Year
Volume
24
Physical description
Dates
published
2015
online
06 - 07 - 2016
Contributors
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Publication order reference
Identifiers
YADDA identifier
bwmeta1.element.ojs-issn-2083-8476-year-2015-volume-24-article-6335
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