TY - JOUR
TI - Incoherent Dictionary Learning for Sparse Representation in Network Anomaly Detection
AU - Andrysiak, Tomasz
AU - Saganowski, Łukasz
TI - Incoherent Dictionary Learning for Sparse Representation in Network Anomaly Detection
AB - 
	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.
VL - 2015
IS - Volume 24
PY - 2016
SN - 1732-3916
C1 - 2083-8476
SP - 63
EP - 71
DO - 10.4467/20838476SI.15.006.3028
UR - https://ejournals.eu/en/journal/schedae-informaticae/article/incoherent-dictionary-learning-for-sparse-representation-in-network-anomaly-detection
KW - Dictionary learning
KW - sparse representation
KW - anomaly detection