Motif-based attack detection in network communication graphs

Authors:
Krzysztof Juszczyszyn;Grzegorz Kolaczek
Affiliations:
Institute of Informatics, Faculty of Computer Science and Management, Wroclaw University of Technology, Wroclaw, Poland;Institute of Informatics, Faculty of Computer Science and Management, Wroclaw University of Technology, Wroclaw, Poland
Venue:
CMS'11 Proceedings of the 12th IFIP TC 6/TC 11 international conference on Communications and multimedia security
Year:
2011

Citing 7
Cited 0

LOF: identifying density-based local outliers

SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Efficient algorithms for mining outliers from large data sets

SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Adaptive, Model-Based Monitoring for Cyber Attack Detection

RAID '00 Proceedings of the Third International Workshop on Recent Advances in Intrusion Detection
Bayesian Event Classification for Intrusion Detection

ACSAC '03 Proceedings of the 19th Annual Computer Security Applications Conference
Efficient sampling algorithm for estimating subgraph concentrations and detecting network motifs

Bioinformatics
FANMOD: a tool for fast network motif detection

Bioinformatics
Efficient Detection of Network Motifs

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)

Quantified Score

Hi-index	0.00

Visualization

Abstract

We propose an original approach which allows the characterization of network communication graphs with the network motifs. As an example we checked our approach by the use of network topology analysis methods applied to communication graphs. We have tested our approach on a simulated attacks inside a scale-free network and data gathered in real networks, showing that the motif distribution reflects the changes in communication pattern and may be used for the detection of ongoing attacks. We have also noticed that the communication graphs of the real networks show a distinctive motif profile.