|Institution:||University of Skövde|
|Keywords:||event prediction; neural networks; machine learning; usage patterns; mobile usage; Natural Sciences; Computer and Information Science; Computer Science; Naturvetenskap; Data- och informationsvetenskap; Datavetenskap (datalogi); Computer Science - Specialization in Systems Development; Datavetenskap - inriktning systemutveckling; Informationsteknologi; Informationsteknologi|
|Full text PDF:||http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-12590|
Security awareness is becoming an increasingly valuable characteristic due to the increased digitization of society. The commonality of constantly connected devices, such as smartphones and tablets, along with the threat of malware and cyber-attacks has sparked an interest in creating a system with the purpose of training people in security awareness. This thesis aims to show the presence of patterns in mobile device usage, and explore the possibility of using pattern detection as a means to predict riskful actions on mobile devices as a step to evaluate the prediction approach for use in the training system.A survey has been conducted by gathering usage data from a number of participants through the use of a logging application. This data was then analyzed using artificial neural networks provided by the open source FANN library in search for patterns preluding certain events. The results lend support to the claim that patterns exist in the way mobile devices are used, but the usefulness of FANN as a tool for finding these patterns was shown to be questionable.