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Windows Based Data Sets for Evaluation of Robustness of Host Based Intrusion Detection Systems (IDS) to Zero-Day and Stealth Attacks

Author

Listed:
  • Waqas Haider

    (School of Engineering and Information Technology, Australian Defence Force Academy, University of New South Wales, Canberra 2052, Australia)

  • Gideon Creech

    (School of Engineering and Information Technology, Australian Defence Force Academy, University of New South Wales, Canberra 2052, Australia)

  • Yi Xie

    (School of Data and Computer Science, Sun Yat-Sen University, Guangzhou 510006, China)

  • Jiankun Hu

    (School of Engineering and Information Technology, Australian Defence Force Academy, University of New South Wales, Canberra 2052, Australia)

Abstract

The Windows Operating System (OS) is the most popular desktop OS in the world, as it has the majority market share of both servers and personal computing necessities. However, as its default signature-based security measures are ineffectual for detecting zero-day and stealth attacks, it needs an intelligent Host-based Intrusion Detection System (HIDS). Unfortunately, a comprehensive data set that reflects the modern Windows OS’s normal and attack surfaces is not publicly available. To fill this gap, in this paper two open data sets generated by the cyber security department of the Australian Defence Force Academy (ADFA) are introduced, namely: Australian Defence Force Academy Windows Data Set (ADFA-WD); and Australian Defence Force Academy Windows Data Set with a Stealth Attacks Addendum (ADFA-WD: SAA). Statistical analysis results based on these data sets show that, due to the low foot prints of modern attacks and high similarity of normal and attacked data, both these data sets are complex, and highly intelligent Host based Anomaly Detection Systems (HADS) design will be required.

Suggested Citation

  • Waqas Haider & Gideon Creech & Yi Xie & Jiankun Hu, 2016. "Windows Based Data Sets for Evaluation of Robustness of Host Based Intrusion Detection Systems (IDS) to Zero-Day and Stealth Attacks," Future Internet, MDPI, vol. 8(3), pages 1-8, July.
    • Handle: RePEc:gam:jftint:v:8:y:2016:i:3:p:29-:d:73345
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    Cited by:

    1. Pooja Chaudhary & Brij Gupta & A. K. Singh, 2022. "Implementing attack detection system using filter-based feature selection methods for fog-enabled IoT networks," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 81(1), pages 23-39, September.

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