Models for Risk assessment of Mobile applications

Abstract: Mobile applications are software that extend the functionality of our smartphones by connecting us with friends and a wide range of other services. Android, which is an operating system based on the Linux kernel, leads the market with over 2.6 million applications recorded on their official store. Application developers, due to the ever-growing innovation in smartphones, are compelled to release new ideas on limited budget and time, resulting in the deployment of malicious applications. Although there exists a security mechanism on the Google Play Store to remove these applications, studies have shown that most of the applications on the app store compromise privacy or pose security-related risks. It is therefore essential to investigate the security risk of installing any of these applications on a device. The objectives are to identify methods and techniques for assessing mobile application security, investigate how attributes indicate the harmfulness of applications, and evaluate the performance of K Nearest Neighbors(K-NN) and Random forest machine learning models in assessing the security risk of installing mobile applications based on information available on the application distribution platform. A literature analysis was done to gather information on the different methods and techniques for assessing security in mobile applications and investigations on how different attributes on the application distribution platform indicate the harmfulness of an application. An experiment was also conducted to examine how various machine learning models perform in evaluating the security risk associated with installing applications, based on information on the application distribution platform. Literature analysis presents the various methods and techniques for mobile application security assessment and identifies how mobile application attributes indicate the harmfulness of mobile applications. The experimental results demonstrate the performance of the aforementioned machine learning models in evaluating the security risk of installing mobile applications. In conclusion, Static, dynamic, and grey-box analysis are the methods used to evaluate mobile application security, and machine learning models including K-NN and Random forest are suitable techniques for evaluating mobile application security risk. Attributes such as the permissions, number of installations, and ratings reveal the likelihood and impact of an underlying security threat. The K-NN and Random forest models when compared to evaluate the security risk of installing mobile applications based on information on the application distribution platform showed high performance with little differences.