Using Weighted Bipartite Graph for Android Malware Classification

Abstract

Abstract-The complexity and the number of mobile malware are increasing continually as the usage of smartphones continue to rise. The popularity of Android has increased the number of malware that target Android-based smartphones. Developing efficient and effective approaches for Android malware classification is emerging as a new challenge. This paper introduces an effective Android malware classifier based on the weighted bipartite graph. This classifier includes two phases: in the first phase, the permissions and API Calls used in the Android app are utilized to construct the weighted bipartite graph; the feature importance scores are integrated as weights in the bipartite graph to improve the discrimination between malware and goodware apps, by incorporating extra meaningful information into the graph structure. The second phase applied multiple classifiers to categorise the Android application as a malware or goodware. The results using an Android malware dataset consists of different malware families, showing the effectiveness of our approach toward Android malware classification.

Mobile Malware Classification via System Calls and Permission for GPS Exploitation

“…Work done by Altaher classified android malware based on weighted bipartite graph [1]. He used API and permission for the classification but the dataset used for the experiment only limited to 500 dataset.…”

“…Furthermore, McAffee has also reported that 37 million of malwares have been detected in apps stores in year 2016. 1 Apart from SMS, call log, audio and picture exploitation, Global Positioning System (GPS) has been used by many attackers to exploit smartphones. Through GPS, attackers know the victims" details such as satellite information and every movement can be monitored by them.…”