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Collective Anomaly Detection Techniques for Network Traffic Analysis

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  • Mohiuddin Ahmed

    (Canberra Institute of Technology)

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.

Suggested Citation

  • Mohiuddin Ahmed, 2018. "Collective Anomaly Detection Techniques for Network Traffic Analysis," Annals of Data Science, Springer, vol. 5(4), pages 497-512, December.
    • Handle: RePEc:spr:aodasc:v:5:y:2018:i:4:d:10.1007_s40745-018-0149-0
    DOI: 10.1007/s40745-018-0149-0
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    References listed on IDEAS

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    1. Struyf, Anja & Hubert, Mia & Rousseeuw, Peter, 1997. "Clustering in an Object-Oriented Environment," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 1(i04).
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    Cited by:

    1. Durgesh Samariya & Amit Thakkar, 2023. "A Comprehensive Survey of Anomaly Detection Algorithms," Annals of Data Science, Springer, vol. 10(3), pages 829-850, June.
    2. Mohiuddin Ahmed & A. K. M. Najmul Islam, 2020. "Deep Learning: Hope or Hype," Annals of Data Science, Springer, vol. 7(3), pages 427-432, September.
    3. Yuan Cui & Yuexing Duan & Yueqin Zhang & Li Pan, 2023. "A Cross-Domain Recommender System for Literary Books Using Multi-Head Self-Attention Interaction and Knowledge Transfer Learning," International Journal of Data Warehousing and Mining (IJDWM), IGI Global, vol. 19(1), pages 1-22, January.

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