Mining sandboxes: Are we there yet?

• Main Authors: BAO, Lingfeng, LE, Tien Duy B., LO, David
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2018
• Subjects: Android Malware; Automated Test Case Generation; Mining Sandboxing; Databases and Information Systems; Information Security; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/4110; https://ink.library.smu.edu.sg/context/sis_research/article/5113/viewcontent/Mining_Sandboxes_2018_SANER_afv.pdf
• Institution: Singapore Management University

The popularity of Android platform on mobile devices has attracted much attention from many developers and researchers, as well as malware writers. Recently, Jamrozik et al. proposed a technique to secure Android applications referred to as mining sandboxes. They used an automated test case generation technique to explore the behavior of the app under test and then extracted a set of sensitive APIs that were called. Based on the extracted sensitive APIs, they built a sandbox that can block access to APIs not used during testing. However, they only evaluated the proposed technique with benign apps but not investigated whether it was effective in detecting malicious behavior of malware that infects benign apps. Furthermore, they only investigated one test case generation tool (i.e., Droidmate) to build the sandbox, while many others have been proposed in the literature. In this work, we complement Jamrozik et al.'s work in two ways: (1) we evaluate the effectiveness of mining sandboxes on detecting malicious behaviors; (2) we investigate the effectiveness of multiple automated test case generation tools to mine sandboxes. To investigate effectiveness of mining sandboxes in detecting malicious behaviors, we make use of pairs of malware and benign app it infects. We build a sandbox based on sensitive APIs called by the benign app and check if it can identify malicious behaviors in the corresponding malware. To generate inputs to apps, we investigate five popular test case generation tools: Monkey, Droidmate, Droidbot, GUIRipper, and PUMA. We conduct two experiments to evaluate the effectiveness and efficiency of these test case generation tools on detecting malicious behavior. In the first experiment, we select 10 apps and allow test case generation tools to run for one hour; while in the second experiment, we select 102 pairs of apps and allow the test case generation tools to run for one minute. Our experiments highlight that 75.5%-77.2% of malware in our dataset can be uncovered by mining sandboxes - showing its power to protect Android apps. We also find that Droidbot performs best in generating test cases for mining sandboxes, and its effectiveness can be further boosted when coupled with other test case generation tools.