Vulnerability Assessment and Malware Analysis of Android Apps Using Machine Learning

Abstract

Mobile devices and their use are rapidly growing to the zenith in the market. Android devices are the most popular and handy when it comes to the mobile devices. With the rapid increase in the use of Android phones, more applications are available for users. Through these alluring multi-functional applications, cyber criminals are stealing personal information and tracking the activities of users. This chapter presents a two-way approach for finding malicious Android packages (APKs) by using different Android applications through static and dynamic analysis. Three cases are considered depending upon the severity level of APK, permission-based protection level, and dynamic analysis of APK for creating the dataset for further analysis. Subsequently, supervised machine learning techniques such as naive Bayes multinomial text, REPtree, voted perceptron, and SGD text are applied to the dataset to classify the selected APKs as malicious, benign, or suspicious.