Android-based Smartphone Malware Exploit Prevention Using a Machine Learning-based Runtime Detection System

In the recent past, Android has emerged as the frontrunner in the smartphone domain when compared to its competitors in terms of global usage. However, this has also led to an increased number of malware attacks targeting Android. To counter this, various anti-malware systems and techniques are available nowadays that offer strong protection to users. The existence and deployment of malware detection models, such as antiviruses, are still made futile by the attackers modifying the code and creating more malware that evades the capability of the detection tools. This presents a larger threat to users' sensitive data by the malware, and that could further lead to data manipulation. This paper aims to contribute to achieving a more secure software engineering experience for Android users by proposing a lightweight tool that enables users to detect such malware more easily and, in turn, protect their overall data security and privacy. The permissions sought by the applications for their functionality are used in this paper to identify and detect such Android malware. A cluster-classification machine learning technique, known as Km-SVM, is used to analyze the application permissions. Our experimental results have shown that the proposed tool can successfully identify Android malware before the user installs it, warning them accordingly in order to avoid data compromise.