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Bayesian Models Applied to Cyber Security Anomaly Detection Problems

Perusquia, J; Griffin, J; Cristiano, V; (2022) Bayesian Models Applied to Cyber Security Anomaly Detection Problems. International Statistical Review , 90 (1) pp. 78-99. 10.1111/insr.12466. Green open access

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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.

Type: Article
Title: Bayesian Models Applied to Cyber Security Anomaly Detection Problems
Open access status: An open access version is available from UCL Discovery
DOI: 10.1111/insr.12466
Publisher version: https://doi.org/10.1111/insr.12466
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.
Keywords: anomaly detection, Bayesian statistics, computer networks, cyber security
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/10130792
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