Technology has been nurturing a wide range of applications in the past decades, assisting humans in automating some of their daily tasks. Nevertheless, more advanced technology systems also expose some potential flaws, which encourage malicious users to explore and break their security. Researchers attempted to overcome such problems by fostering intrusion detection systems, which are security layers that try to detect mischievous attempts. Apart from that, increasing demand for machine learning also enabled the possibility of combining such approaches in order to provide more robust detection systems. In this context, we introduce a novel approach to deal with anomaly detection, where instead of using the problem’s raw features, we project them through a restricted Boltzmann machine. The intended approach was assessed under a well-known literature anomaly detection dataset and achieved suitable results, better than some state-of-the-art approaches.

在过去的几十年中，技术一直在促进广泛的应用，从而帮助人们自动化一些日常任务。但是，更先进的技术系统也会暴露出一些潜在的缺陷，这些缺陷会鼓励恶意用户探索并破坏其安全性。研究人员试图通过建立入侵检测系统来克服这些问题，入侵检测系统是试图检测错误尝试的安全层。除此之外，对机器学习的不断增长的需求也使组合此类方法以提供更强大的检测系统成为可能。在这种情况下，我们引入了一种新颖的方法来处理异常检测，在这种方法中，我们不使用问题的原始特征，而是通过受限的Boltzmann机器对它们进行投影。