Prediction of Android Malicious Software Using Boosting Algorithms

Abstract

Android malware, a group of malicious software variants, including viruses, ransomware and spyware, designed to cause substantial damage to data and systems or to access a network without authorization. With an inexorable shift in technology, Android has supplanted other Mobile platforms by being flexible and user-friendly to the users. As the number of Android apps continues to grow every day, the number of malwares aimed at attacking those users is also on the rise. Thus, it becomes emergent to identify and remove malicious Android applications before installation to prevent user’s loss. Several studies have already been carried out to anticipate Android malware using machine learning algorithms, while as per the literature survey conducted by this study, a significant research has not been found to be focusing especially on the genre of boosting algorithms. Therefore, the objective of this paper is to classify malicious and benign Android applications by using Boosting algorithm. To attain the research objective, four widely defined boosting models viz. AdaBoost, CatBoost, XGBoost, and GradientBoost were developed whereas, it was found that CatBoost and GradientBoost had the highest F1 score (93.9%), followed by Adaboost (F1 score 93.5%), and XGBoost (F1 score 93.5%).