NetFlow anomaly detection through parallel cluster density analysis in continuous time-series.
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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.
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