Collective Anomaly Detection Techniques for Network Traffic Analysis

Mohiuddin Ahmed ()
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Mohiuddin Ahmed: Canberra Institute of Technology

Annals of Data Science, 2018, vol. 5, issue 4, No 1, 497-512

Abstract: Abstract In certain cyber-attack scenarios, such as flooding denial of service attacks, the data distribution changes significantly. This forms a collective anomaly, where some similar kinds of normal data instances appear in abnormally large numbers. Since they are not rare anomalies, existing anomaly detection techniques cannot properly identify them. This paper investigates detecting this behaviour using the existing clustering and co-clustering based techniques and utilizes the network traffic modelling technique via Hurst parameter to propose a more effective algorithm combining clustering and Hurst parameter. Experimental analysis reflects that the proposed Hurst parameter-based technique outperforms existing collective and rare anomaly detection techniques in terms of detection accuracy and false positive rates. The experimental results are based on benchmark datasets such as KDD Cup 1999 and UNSW-NB15 datasets.

Keywords: Collective anomaly; Hurst parameter; Clustering; Denial of service; Cyber attack (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s40745-018-0149-0

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