NetFlow anomaly detection though parallel cluster density analysis in continuous time-series
Authors: Flanagan, K., Fallon, E., Connolly, P. and Awad, A.
Journal: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume: 10372 LNCS
Pages: 221-232
eISSN: 1611-3349
ISBN: 9783319613819
ISSN: 0302-9743
DOI: 10.1007/978-3-319-61382-6_18
Abstract:The increase in malicious network based attacks has resulted in a growing interest in network anomaly detection. The ability to detect unauthorized or malicious activity on a network is of importance to any organization. With the increase in novel attacks, anomaly detection techniques can be more successful in detecting unknown malicious activity in comparison to traditional signature based methods. However, in a real-world environment, there are many variables that cannot be simulated. This paper proposes an architecture where parallel clustering algorithms work concurrently in order to detect abnormalities that may be lost while traversing over time-series windows. The presented results describe the NetFlow activity of the NPD Group, Inc. over a 24-hour period. The presented results contain real-world anomalies that were detected.
Source: Scopus