Incoherent Dictionary Learning for Sparse Representation in Network Anomaly Detection

• Authors: Tomasz Andrysiak , Łukasz Saganowski
• Languages of publication: 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.

Keywords

PL

Dictionary learning, sparse representation, anomaly detection

• Publisher: [unknown2]
• Journal: Schedae Informaticae
• Year: 2015
• Volume: 24

Dates

published

2015

online

06 - 07 - 2016

Contributors

author

Tomasz Andrysiak

author

Łukasz Saganowski