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Android's security model severely limits the capabilities of anti-malwaresoftware. Unlike commodity anti-malware solutions on desktop systems, theirAndroid counterparts run as sandboxed applications without root privileges andare limited by Android's permission system. As such, PHAs on Android areusually willingly installed by victims, as they come disguised as usefulapplications with hidden malicious functionality, and are encountered on mobileapp stores as suggestions based on the apps that a user previously installed.Users with similar interests and app installation history are likely to beexposed and to decide to install the same PHA. This observation gives us theopportunity to develop predictive approaches that can warn the user about whichPHAs they will encounter and potentially be tempted to install in the nearfuture. These approaches could then be used to complement commodityanti-malware solutions, which are focused on post-fact detection, closing thewindow of opportunity that existing solutions suffer from. In this paper wedevelop Andruspex, a system based on graph representation learning, allowing usto learn latent relationships between user devices and PHAs and leverage themfor prediction. We test Andruspex on a real world dataset of PHA installationscollected by a security company, and show that our approach achieves very highprediction results (up to 0.994 TPR at 0.0001 FPR), while at the same timeoutperforming alternative baseline methods. We also demonstrate that Andruspexis robust and its runtime performance is acceptable for a real worlddeployment.

ANDRUSPEX : Leveraging Graph Representation Learning to Predict Harmful App Installations on Mobile Devices