Twitter-enhanced Android malware detection

Abstract

In data-driven Android malware detection, large numbers of both malicious and benign apps are used to train machine learning classifiers to detect malware. Existing approaches have nearly exclusively focused on app contents to extract features for classification. We seek to understand if auxiliary data, specifically Twitter data, can be used to improve the performance of existing approaches for Android malware detection. Throughout the course of our research, we collected over 50 million tweets potentially related to Android apps. We propose to link tweets with apps using approaches inspired from the standard vector space model, and subsequently study the usefulness of the linked tweets in malware detection. We find that Twitter data accurately linked to apps through HTTP links can be used to improve the machine learning classifier performance across a variety of common malware detection classifiers. However, classification experiments with Twitter data automatically linked to apps reveal the need for future work on more robust linking approaches.