Static, dynamic and intrinsic features based android malware detection using machine learning

Abstract

Android is one of the smartest and advanced operating systems in the mobile phone market in the current era. The number of smartphone users based on the Android platform is rising swiftly which increases its popularity all over the world. The rising fame of this technology attracts everyone toward it and invites more number of hackers in Android platform. These hackers spread malicious application in the market and lead to the high chance of data leakage, financial loss and other damages. Therefore, malware detection techniques should be implemented to detect the malware smartly. Different techniques have been proposed using permission-based or system call-based approaches. In this paper, a hybrid approach of static, dynamic and intrinsic features based malware detection using k-nearest neighbors (k-NN) and logistic regression machine learning algorithms. The intrinsic feature contribution has also been evaluated. Furthermore, linear discriminant analysis technique has been implemented to evaluate the impact on the detection rate. The calculation uses a publicly available dataset of Androtrack. Based on the estimation results, both the k-nearest neighbors (k-NN) and logistic regression classifiers produced accuracy of 97.5%.