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Yinxing Xue

Publications and source records attributed to Yinxing Xue.

20 records · Page 2Linked to original sources

An Empirical Assessment of Security Risks of Global Android Banking Apps

Mobile banking apps, belonging to the most security-critical app category, render massive and dynamic transactions susceptible to security risks. Given huge potential financial loss caused by vulnerabilities, existing research lacks a comprehensive empirical study on the security risks of global banking apps to provide useful insights and improve the security of banking apps. Since data-related weaknesses in banking apps are critical and may directly cause serious financial loss, this paper first revisits the state-of-the-art available tools and finds that they have limited capability in identifying data-related security weaknesses of banking apps. To complement the capability of existing tools in data-related weakness detection, we propose a three-phase automated security risk assessment system, named AUSERA, which leverages static program analysis techniques and sensitive keyword identification. By leveraging AUSERA, we collect 2,157 weaknesses in 693 real-world banking apps across 83 countries, which we use as a basis to conduct a comprehensive empirical study from different aspects, such as global distribution and weakness evolution during version updates. We find that apps owned by subsidiary banks are always less secure than or equivalent to those owned by parent banks. In addition, we also track the patching of weaknesses and receive much positive feedback from banking entities so as to improve the security of banking apps in practice. To date, we highlight that 21 banks have confirmed the weaknesses we reported. We also exchange insights with 7 banks, such as HSBC in UK and OCBC in Singapore, via in-person or online meetings to help them improve their apps. We hope that the insights developed in this paper will inform the communities about the gaps among multiple stakeholders, including banks, academic researchers, and third-party security companies.

cs.CR↗

AndroVault: Constructing Knowledge Graph from Millions of Android Apps for Automated Analysis

Data driven research on Android has gained a great momentum these years. The abundance of data facilitates knowledge learning, however, also increases the difficulty of data preprocessing. Therefore, it is non-trivial to prepare a demanding and accurate set of data for research. In this work, we put forward AndroVault, a framework for the Android research composing of data collection, knowledge representation and knowledge extraction. It has started with a long-running web crawler for data collection (both apps and description) since 2013, which guarantees the timeliness of data; With static analysis and dynamic analysis of the collected data, we compute a variety of attributes to characterize Android apps. After that, we employ a knowledge graph to connect all these apps by computing their correlation in terms of attributes; Last, we leverage multiple technologies such as logical inference, machine learning, and correlation analysis to extract facts (more accurate and demanding, either high level or not, data) that are beneficial for a specific research problem. With the produced data of high quality, we have successfully conducted many research works including malware detection, code generation, and Android testing. We would like to release our data to the research community in an authenticated manner, and encourage them to conduct productive research.

cs.SE↗