ABC: android botnet classification using feature selection and classification algorithms - Abdullah, Zubaile; Mohd Saudi, Madihah; Anuar, Nor Badrul (American Scientific Publishers, 2017)
Smartphones have become an important part of human lives, and this led to an increase number of smartphone users. However, this also attracts hackers to develop malicious applications especially Android botnet to steal the private information and causing financial losses. Due to the fast modifications in the technologies used by malicious application (app) developers, there is an urgent need for more advanced techniques for Android botnet detection. In this paper, a new approach for Android botnet classification based on features selection and classification algorithms is proposed. The proposed approach uses the permissions requested in the Android app as features, to differentiate between the Android botnet apps and benign apps. The Information Gain algorithm is used to select the most significant permissions, then the classification algorithms Naïve Bayes, Random Forest and J48 used to classify the Android apps as botnet or benign apps. The experimental results show that Random Forest Algorithm achieved the highest detection accuracy of 94.6% with lowest false positive rate of 0.099.