A distributed architecture for phishing detection using Bayesian Additive Regression Trees

With the variety of applications in mobile devices, such devices are no longer deemed calling gadgets merely. Various applications are used to browse the Internet, thus access financial data, and store sensitive personal information. In consequence, mobile devices are exposed to several types of attacks. Specifically, phishing attacks can easily take advantage of the limited or lack of security and defense applications therein. Furthermore, the limited power, storage, and processing capabilities render machine learning techniques inapt to classify phishing and spam emails in such devices. The present study proposes a distributed architecture hinging on machine learning approaches to detect phishing emails in a mobile environment based on a modified version of Bayesian additive regression trees (BART). Apparently, BART suffers from high computational time and memory overhead, therefore, distributed algorithms are proposed to accommodate detection applications in resource constrained wireless environments.