IDEAS home Printed from https://ideas.repec.org/a/spr/aodasc/v5y2018i4d10.1007_s40745-018-0149-0.html
   My bibliography  Save this article

Collective Anomaly Detection Techniques for Network Traffic Analysis

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s40745-018-0149-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s40745-018-0149-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kauffmann, Albrecht, 2012. "Delineation of City Regions Based on Commuting Interrelations: The Example of Large Cities in Germany," IWH Discussion Papers 4/2012, Halle Institute for Economic Research (IWH).
    2. Hornik, Kurt, 2005. "A CLUE for CLUster Ensembles," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 14(i12).
    3. Beata Gavurova & Ladislav Suhanyi & Martin Rigelský, 2020. "Tourist spending and productivity of economy in OECD countries – research on perspectives of sustainable tourism," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 8(1), pages 983-1000, September.
    4. Kim, Jaejik & Billard, L., 2011. "A polythetic clustering process and cluster validity indexes for histogram-valued objects," Computational Statistics & Data Analysis, Elsevier, vol. 55(7), pages 2250-2262, July.
    5. Jörg Weking & Andreas Hein & Markus Böhm & Helmut Krcmar, 2020. "A hierarchical taxonomy of business model patterns," Electronic Markets, Springer;IIM University of St. Gallen, vol. 30(3), pages 447-468, September.
    6. Tommaso Agasisti & Francesca Ieva & Anna Maria Paganoni, 2017. "Heterogeneity, school-effects and the North/South achievement gap in Italian secondary education: evidence from a three-level mixed model," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 26(1), pages 157-180, March.
    7. Ma, Zhenjun & Yan, Rui & Nord, Natasa, 2017. "A variation focused cluster analysis strategy to identify typical daily heating load profiles of higher education buildings," Energy, Elsevier, vol. 134(C), pages 90-102.
    8. Renato Cordeiro Amorim & Vladimir Makarenkov & Boris Mirkin, 2020. "Core Clustering as a Tool for Tackling Noise in Cluster Labels," Journal of Classification, Springer;The Classification Society, vol. 37(1), pages 143-157, April.
    9. Wen, Xuanhao & Cao, Huajun & Li, Hongcheng & Zheng, Jie & Ge, Weiwei & Chen, Erheng & Gao, Xi & Hon, Bernard, 2022. "A dual energy benchmarking methodology for energy-efficient production planning and operation of discrete manufacturing systems using data mining techniques," Energy, Elsevier, vol. 255(C).
    10. Karpinska, Lilia & Śmiech, Sławomir, 2021. "Breaking the cycle of energy poverty. Will Poland make it?," Energy Economics, Elsevier, vol. 94(C).
    11. Jesus Gonzalez-Feliu & Joelle Morana & Josep-Maria Salanova Grau & Tai-Yu Ma, 2013. "Design And Scenario Assessment For Collaborative Logistics And Freight Transport Systems," Articles, International Journal of Transport Economics, vol. 40(2).
    12. Frederickson Entila & Xiaowei Han & Akira Mine & Paul Schulze-Lefert & Kenichi Tsuda, 2024. "Commensal lifestyle regulated by a negative feedback loop between Arabidopsis ROS and the bacterial T2SS," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    13. Jörg Weking & Michael Mandalenakis & Andreas Hein & Sebastian Hermes & Markus Böhm & Helmut Krcmar, 2020. "The impact of blockchain technology on business models – a taxonomy and archetypal patterns," Electronic Markets, Springer;IIM University of St. Gallen, vol. 30(2), pages 285-305, June.
    14. Alexander Platzer, 2013. "Visualization of SNPs with t-SNE," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-6, February.
    15. Albrecht Kauffmann, 2011. "Wirkung kommunaler Investitionen in die Tourismusinfrastruktur am Beispiel Sachsens," Review of Regional Research: Jahrbuch für Regionalwissenschaft, Springer;Gesellschaft für Regionalforschung (GfR), vol. 31(1), pages 57-73, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:aodasc:v:5:y:2018:i:4:d:10.1007_s40745-018-0149-0. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.