Risk prediction of malware victimization based on user behavior

Understanding what types of users and usage are more conducive to malware infections is crucial if we want to establish adequate strategies for dealing and mitigating the effects of computer crime in its various forms. Real-usage data is therefore essential to make better evidence-based decisions that will improve users' security. To this end, we performed a 4-month field study with 50 subjects and collected real-usage data by monitoring possible infections and gathering data on user behavior. In this paper, we present a first attempt at predicting risk of malware victimization based on user behavior. Using neural networks we developed a predictive model that has an accuracy of up to 80% at predicting user's likelihood of being infected.