On the Feasibility of Optimum-Path Forest in the Context of Internet-of-Things-Based Applications

The “Internet-of-Things" (IoT) paradigm has been extensively focused in the last years, since it covers a wide scope of applications, that range from spam detection in e-mail datasets to on-body sensors. However, this data flooding, also known as “big data" phenomenon, has required high levels of security, since private and important data are now surfing over our heads. In this paper, we introduced a recently developed pattern recognition technique called Optimum-Path Forest (OPF) for the task of data mining in IoT-oriented applications, such as spam detection in e-mail- and web-content, as well as intrusion detection in computer networks. An extensive comparison against fourteen classification techniques can drive us a picture about the effectiveness and efficiency of OPF classifier in that context.