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Modeling and predicting extreme cyber attack rates via marked point processes

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  • 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
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    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.

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