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2015-05-04
Shao Shuai, Dong Guowei, Guo Tao, Yang Tianchang, Shi Chenjie.  2014.  Modelling Analysis and Auto-detection of Cryptographic Misuse in Android Applications. Dependable, Autonomic and Secure Computing (DASC), 2014 IEEE 12th International Conference on. :75-80.

Cryptographic misuse affects a sizeable portion of Android applications. However, there is only an empirical study that has been made about this problem. In this paper, we perform a systematic analysis on the cryptographic misuse, build the cryptographic misuse vulnerability model and implement a prototype tool Crypto Misuse Analyser (CMA). The CMA can perform static analysis on Android apps and select the branches that invoke the cryptographic API. Then it runs the app following the target branch and records the cryptographic API calls. At last, the CMA identifies the cryptographic API misuse vulnerabilities from the records based on the pre-defined model. We also analyze dozens of Android apps with the help of CMA and find that more than a half of apps are affected by such vulnerabilities.
 

Shao Shuai, Dong Guowei, Guo Tao, Yang Tianchang, Shi Chenjie.  2014.  Analysis on Password Protection in Android Applications. P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC), 2014 Ninth International Conference on. :504-507.

Although there has been much research on the leakage of sensitive data in Android applications, most of the existing research focus on how to detect the malware or adware that are intentionally collecting user privacy. There are not much research on analyzing the vulnerabilities of apps that may cause the leakage of privacy. In this paper, we present a vulnerability analyzing method which combines taint analysis and cryptography misuse detection. The four steps of this method are decompile, taint analysis, API call record, cryptography misuse analysis, all of which steps except taint analysis can be executed by the existing tools. We develop a prototype tool PW Exam to analysis how the passwords are handled and if the app is vulnerable to password leakage. Our experiment shows that a third of apps are vulnerable to leak the users' passwords.