MaplDroid: Malicious Android Application Detection based on Naive Bayes using Multiple

Abstract

Android is currently the most popular operating system for mobile devices in the market. Android device is being used by every other person for everyday life activities and it has become a centre for storing personal information. Because of these reasons it attracts many hackers, who develop malicious software for attacking the platform; thus a technique that can effectively prevent the system from malware attacks is required. In this paper, an malware detection technique, MaplDroid has been proposed for detecting malware applications on Android platform. The proposed technique statically analyses the application files using features which are extracted from the manifest file. A supervised learning model based on Naive Bayes is used to classify the application as benign or malicious. MaplDroid achieved Recall score 99.12% and F1 score 83.45%.