Enhancing anomaly detection through restricted Boltzmann machine features projection - Rosa, Gustavo H. de [UNESP]; Roder, Mateus [UNESP]; Santos, Daniel F. S. [UNESP]; Costa, Kelton A. P. [UNESP] (Springer Nature, 2021)
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.
Keywords: Anomaly detection; Intrusion detection system; Machine learning; Restricted Boltzmann machine