Android Malware Detection: A Machine Leaning Approach

-- This study examines machine learning techniques like Decision Trees, Support V ector Machines, Logistic Regression, Neural Networks, and ensemble methods to detect Android malware. The study evaluates these models on a dataset of Android applications and analyzes their accuracy, efficiency, and real-world applicability. Key findings show that ensemble methods demonstrate superior performance, but there are trade-offs between model interpretability, efficiency, and accuracy. Given its increasing threat, the insights guide future research and practical use of ML to combat Android malware. I. INTRODUCTION Smartphones have brought in a new era of connectivity, convenience, and innovation, with Android being the most widely used mobile operating system [1], [2]. However, this ubiquity has come with challenges. The background of Android's ecosystem makes clear that the characteristics that make Android popular also leave it vulnerable to malicious activities. Specifically, Android's open-source nature, vast user base, and easy application distribution and installation have created an environment where cybercriminals can thrive. Thus, it is essential to understand the Android ecosystem's unique landscape to address the severe threat of Android malware. The following section sets the stage for exploring advanced malware detection techniques for Android devices in later sections. A. Background The extensive adoption of Android operating systems, with their open-source nature and customization capabilities, has led to them becoming a primary target for cybercriminals. Android's vast and diverse application ecosystem presents significant security challenges, as malicious applications can masquerade as legitimate ones, exploiting vulnerabilities and employing social engineering tactics [1]-[3]. These malicious activities include stealing sensitive information, sending premium-rate SMS messages, and installing additional payloads [4]-[5].