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Bayesian Models Applied to Cyber Security Anomaly Detection Problems

Author

Listed:
  • José A. Perusquía
  • Jim E. Griffin
  • Cristiano Villa

Abstract

Cyber security is an important concern for all individuals, organisations and governments globally. Cyber attacks have become more sophisticated, frequent and dangerous than ever, and traditional anomaly detection methods have been proved to be less effective when dealing with these new classes of cyber threats. In order to address this, both classical and Bayesian models offer a valid and innovative alternative to the traditional signature‐based methods, motivating the increasing interest in statistical research that it has been observed in recent years. In this review, we provide a description of some typical cyber security challenges, typical types of data and statistical methods, paying special attention to Bayesian approaches for these problems.

Suggested Citation

  • José A. Perusquía & Jim E. Griffin & Cristiano Villa, 2022. "Bayesian Models Applied to Cyber Security Anomaly Detection Problems," International Statistical Review, International Statistical Institute, vol. 90(1), pages 78-99, April.
    • Handle: RePEc:bla:istatr:v:90:y:2022:i:1:p:78-99
    DOI: 10.1111/insr.12466
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    References listed on IDEAS

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    2. Alexander D. Bolton & Nicholas A. Heard, 2018. "Malware Family Discovery Using Reversible Jump MCMC Sampling of Regimes," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(524), pages 1490-1502, October.
    3. Matthew Price‐Williams & Nick Heard & Patrick Rubin‐Delanchy, 2019. "Detecting weak dependence in computer network traffic patterns by using higher criticism," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 68(3), pages 641-655, April.
    4. Xi Chen & Kaoru Irie & David Banks & Robert Haslinger & Jewell Thomas & Mike West, 2018. "Scalable Bayesian Modeling, Monitoring, and Analysis of Dynamic Network Flow Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(522), pages 519-533, April.
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    1. Perusquía, José A. & Griffin, Jim E. & Villa, Cristiano, 2025. "Beta-CoRM: A Bayesian approach for n-gram profiles analysis," Computational Statistics & Data Analysis, Elsevier, vol. 202(C).

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