Date Thesis Awarded
Honors Thesis -- Open Access
Bachelors of Science (BS)
With Android devices becoming more advanced and gaining more popularity, the number of cryptographic-API misuses in mobile applications is escalating. Numerous snippets of code in Android are from Stack Overflow and over 90% of them contain several crypto-issues. Various crypto-misuse detectors come out aiming to report vulnerabilities of apps and better secure users’ privacy. These detectors can be broadly classified into two categories based on the analysis strategies employed to catch misuses – static analysis (i.e., by scanning the code base) and dynamic analysis (i.e., by executing the code). However, there are not enough research on comparing their underlying differences, making it difficult to explain the pervasiveness of static crypto-detectors in both academia and industry. The lack of studies potentially limits the improvement of crypto-detection efficiency. In this study, a holistic evaluation and comparison on static and dynamic analysis’ underlying mechanisms, robustness, and efficiency are carried out. A systematic empirical experiment is implemented on testing 1003 popular Android applications across 21 categories from Google Play. We find that 93.3% of the apps make at least one mistake using cryptographic APIs and closely analyze top four cryptographic rules reported to be violated most frequently by static crypto detector. Instead of merely comparing statistics such as false positives (i.e., false alarms), we focus on examining the crypto rules whose number of violations reported by static and dynamic crypto detectors diverge greatly. In addition, we firstly posit a new taxonomy schema that classifies cryptographic rules based on how they are inspected rather than their attack type or severity level. This schema will be useful to both researchers and practitioners to decide how to efficiently combine static and dynamic techniques to improve the reliability and accuracy of crypto-detection.
Li, Kunyang, "Static and Dynamic Analysis in Cryptographic-API Misuse Detection of Mobile Application" (2021). Undergraduate Honors Theses. William & Mary. Paper 1739.