A review on mobile threats and machine learning based detection approaches
Bilgehan Arslan, Sedef Gunduz, Şeref Sağıroğlu · 2016
The research of mobile threats detection using machine learning algorithms have got much attention in recent years due to increase of attacks. In this paper, mobile vulnerabilities were examined based on attack types. In order to prevent or detect these attacks machine learning methods used were analyzed and papers published in between 2009 and 2014 have been evaluated. Most important mobile vulnerabilities implementation format for these threats, detection methods and prevention approaches with the help of machine learning algorithms are presented. The obtained results are compared from their achievements were summarized. The results have shown that selecting and using datasets play an important role on the success of the system. Additionally, supervised learning techniques produce better results while compared with unsupervised ones in intrusion detection.