Viability of Machine Learning in Android Scareware Detection

Abstract

With the rapid increase in the use of mobile phones and other technologies, there has been a proportional growth in malware that tries to collect sensitive user data. Android is the most popular operating system for smartphones and has a great potential of becoming a target for malware threats. Scareware is a type of malware that tries to get users to provide valuable information or download malicious software through social engineering. This research aims to find out if machine learning is a viable option to prevent the consequences of scareware by accurately detecting them. In this investigation, four supervised machine learning (ML) algorithms were used in a dataset called CICAndMal2017 with 85 attributes for each of 11 Android scareware families and benign samples. We were able to achieve an accuracy of 96% which helped us to conclude that machine learning can and should be used to detect scareware. The machine learning models were then tested to calculate the accuracy for classifying each scareware family.