Bayesian Models Applied to Cyber Security Anomaly Detection Problems

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.

References in corpus (6)

• Variational Inference: A Review for Statisticians
• Scalable Bayesian modeling, monitoring and analysis of dynamic network flow data
• Robust PCA for Anomaly Detection in Cyber Networks
• Stochastic identification of malware with dynamic traces
• Nearly Optimal Change-Point Detection with an Application to Cybersecurity
• Extending Dynamic Bayesian Networks for Anomaly Detection in Complex Logs