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.

中文翻译：

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应用于网络安全异常检测问题的贝叶斯模型

网络安全是全球所有个人、组织和政府关注的重要问题。网络攻击变得比以往任何时候都更加复杂、频繁和危险，而传统的异常检测方法已被证明在处理这些新型网络威胁时效率较低。为了解决这个问题，经典模型和贝叶斯模型都为传统的基于签名的方法提供了一种有效且创新的替代方案，从而激发了人们对近年来观察到的统计研究的日益浓厚的兴趣。在这篇评论中，我们描述了一些典型的网络安全挑战、典型的数据类型和统计方法，特别关注了针对这些问题的贝叶斯方法。