Spyware Identification for Android Systems Using Fine Trees

Abstract

Android operating system (OS) has been recently featured as the most commonly used and ingratiated OS for smartphone ecosystems. This is due to its high interoperability as an open-source platform and its compatibility with all the major browsers within the mobile ecosystem. However, android is susceptible to a wide range of Spyware traffic that can endanger a mobile user in many ways, like password stealing and recording patterns of a user. This paper presents a spyware identification schemes for android systems making use of three different machine learning schemes, including fine decision trees (FDT), support vector machines (SVM), and the naïve Bayes classifier (NBC). The constructed models have been evaluated on a novel dataset (Spyware-Android 2022) using several performance measurement units such as accuracy, precision, and sensitivity. Our experimental simulation tests revealed the notability of the model-based FDT, making the peak accuracy 98.2%. The comparison with the state-of-art spyware identification models for android systems showed that our proposed model had improved the model’s accuracy by more than 18%.