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Like any other software engineering activity, assessing the security of a software system entails prioritizing the resources and minimizing the risks. Techniques ranging from the manual inspection to automated static and dynamic analyses are commonly employed to identify security vulnerabilities prior to the release of the software. However, none of these techniques is perfect, as static analysis is prone to producing lots of false positives and negatives, while dynamic analysis and manual inspection are unwieldy, both in terms of required time and cost. This research aims to improve these techniques by mining relevant information from vulnerabilities found in the app markets. The approach relies on the fact that many modern software systems, in particular mobile software, are developed using rich application development frameworks (ADF), allowing us to raise the level of abstraction for detecting vulnerabilities and thereby making it possible to classify the types of vulnerabilities that are encountered in a given category of application. By coupling this type of information with severity of the vulnerabilities, we are able to improve the efficiency of static and dynamic analyses, and target the manual effort on the riskiest vulnerabilities.

Mining mobile app markets for prioritization of security assessment effort