A Comparative Study of Machine Learning Techniques for Android Malware Detection

The rapid growth and wide availability of Android applications in recent years has resulted in a spike in the number of sophisticated harmful applications targeting Android users. Because of the popularity and amount of open-sourced supported features of Android OS, cyber attackers prefer to target Android-based devices over other smartphones. Malicious programs endanger user privacy and device integrity. To address this issue, the authors investigated machine learning algorithms for detecting malware in Android in this study. They employed a static analysis approach, collecting permissions from each application's APK and then generating feature vectors based on the extracted permissions. Finally, they trained several machine learning algorithms to create classification models that can distinguish between benign and malicious applications. According to experimental findings, random forest and multi-layer perceptron approaches, which have accuracy levels of 95.4% and 95.1%, respectively, have the best classification performance.