並列摘要
For security and privacy considerations, it is important for Android users to understand the behavior and the risk of an application. Although Google claims that new applications available on the official market have passed their security checks, the open design of the Android system still allows a user to install applications for third party vendors. Therefore, there is still a demand for users to know more about an unknown application. In this paper, we discussed our experiences on setting up a scalable automated Android behavior monitor using virtualization techniques. Our contribution is two-fold: (1) We design and implement a scalable behavior monitor using both dynamic analysis and static analysis techniques; and (2) Based on parts of the analyzed results, we develop a preliminary filter to distinguish benign and malicious applications. The system is open to the public and we expect that the analyzed results can be fed back to the research community and further stimulate more studies on analyzing malicious Android applications.
延伸閱讀
- Chuang, H. Y. (2014). Machine learning based hybrid behavior model for Android malware analysis [master's thesis, National Taiwan University]. Airiti Library. https://doi.org/10.6342/NTU.2014.02281
- Chang, W. L. (2015). An Android Behavior-Based Malware Detection using Machine Learning [master's thesis, National Tsing Hua University]. Airiti Library. https://www.airitilibrary.com/Article/Detail?DocID=U0016-0312201510295557
- 李蕙君（2012）。Study of Android’s Application Automated Testing〔碩士論文，國立臺灣大學〕。華藝線上圖書館。https://doi.org/10.6342/NTU.2012.02433
- 黃上誼（2016）。Automated Configuration Testing and Coverage Analysis for Android Apps〔碩士論文，國立臺灣大學〕。華藝線上圖書館。https://doi.org/10.6342/NTU201602249
- 曾國書（2012）。Personalized Application Management System based on the Android Platform〔碩士論文，淡江大學〕。華藝線上圖書館。https://doi.org/10.6846/TKU.2012.00201