Android Botnet Detection Using Hybrid Analysis

Botnet pandemics are becoming more prevalent with the growing use of mobile phone technologies.Mobile phone technologies provide a wide range of applications, including entertainment, commerce, education, and finance.In addition, botnet refers to the collection of compromised devices managed by a botmaster and engaging with each other via a command server to initiate an attack including phishing email, ad-click fraud, blockchain, and much more.As the number of botnet attacks rises, detecting harmful activities is becoming more challenging in handheld devices.Therefore, it is crucial to evaluate mobile botnet assaults to find the security vulnerabilities that occur through coordinated command servers causing major financial and ethical harm.For this purpose, we propose a hybrid analysis approach that integrates permissions and API and experiments on the machine-learning classifiers to detect mobile botnet applications.In this paper, the experiment employed benign, botnet, and malware applications for validation of the performance and accuracy of classifiers.The results conclude that a classifier model based on a simple decision tree obtained 99% accuracy with a low 0.003 false-positive rate than other machine learning classifiers for botnet applications detection.As an outcome of this paper, a hybrid approach enhances the accuracy of mobile botnet detection as compared to static and dynamic features when both are taken separately.