IDEAS home Printed from https://ideas.repec.org/a/taf/japsta/v44y2017i14p2534-2563.html
   My bibliography  Save this article

Modeling and predicting extreme cyber attack rates via marked point processes

Author

Listed:
  • Chen Peng
  • Maochao Xu
  • Shouhuai Xu
  • Taizhong Hu

Abstract

Cyber attacks have become a problem that is threatening the economy, human privacy, and even national security. Before we can adequately address the problem, we need to have a crystal clear understanding about cyber attacks from various perspectives. This is a challenge because the Internet is a large-scale complex system with humans in the loop. In this paper, we investigate a particular perspective of the problem, namely the extreme value phenomenon that is exhibited by cyber attack rates, which are the numbers of attacks against a system of interest per time unit. It is important to explore this perspective because understanding the statistical properties of extreme cyber attack rates will pave the way for cost-effective, if not optimal, allocation of resources in real-life cyber defense operations. Specifically, we propose modeling and predicting extreme cyber attack rates via marked point processes, while using the Value-at-Risk as a natural measure of intense cyber attacks. The point processes are then applied to analyze some real data sets. Our analysis shows that the point processes can describe and predict extreme cyber attack rates at a very satisfactory accuracy.

Suggested Citation

  • Chen Peng & Maochao Xu & Shouhuai Xu & Taizhong Hu, 2017. "Modeling and predicting extreme cyber attack rates via marked point processes," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(14), pages 2534-2563, October.
    • Handle: RePEc:taf:japsta:v:44:y:2017:i:14:p:2534-2563
    DOI: 10.1080/02664763.2016.1257590
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/02664763.2016.1257590
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/02664763.2016.1257590?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.

    Citations

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


    Cited by:

    1. Levitin, Gregory & Xing, Liudong & Xiang, Yanping, 2020. "Optimal early warning defense of N-version programming service against co-resident attacks in cloud system," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    2. Md. Hamid Uddin & Md. Hakim Ali & Mohammad Kabir Hassan, 2020. "Cybersecurity hazards and financial system vulnerability: a synthesis of literature," Risk Management, Palgrave Macmillan, vol. 22(4), pages 239-309, December.
    3. Hillairet, Caroline & Réveillac, Anthony & Rosenbaum, Mathieu, 2023. "An expansion formula for Hawkes processes and application to cyber-insurance derivatives," Stochastic Processes and their Applications, Elsevier, vol. 160(C), pages 89-119.
    4. Luo, Liang & Xing, Liudong & Levitin, Gregory, 2019. "Optimizing dynamic survivability and security of replicated data in cloud systems under co-residence attacks," Reliability Engineering and System Safety, Elsevier, vol. 192(C).
    5. Shouhuai Xu & Moti Yung & Jingguo Wang, 2021. "Seeking Foundations for the Science of Cyber Security," Information Systems Frontiers, Springer, vol. 23(2), pages 263-267, April.

    More about this item

    Statistics

    Access and download statistics

    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:taf:japsta:v:44:y:2017:i:14:p:2534-2563. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/CJAS20 .

    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.