Dynamic analysis that automatically analyzes malware has become the defacto standard for coping with the huge amount of current malware types. One analysis support is a function that maps the malware behavior to each element of the MITRE ATT&CK^® Technique. This function has been adopted in many online sandboxes and contributes to the efficiency of analysis. On the other hand, this function depends on the implementation of the mapping rules, which may affect the analysis results. Therefore, we investigated the actual situation of online sandboxes that have a function for mapping to the attack technique. In this study, we analyzed a total of 26,078 malware analysis results from three online sandboxes, found that the characteristics for matching to each technique differed among the sandboxes, and clarified the ease of matching each technique. We also compared the mapping characteristics of techniques with those of static analysis-based techniques and manually written reports and showed that the mapping characteristics differed among the techniques. Furthermore, we derived best practices for utilization on the basis of each survey. We believe that these results will lead to a better understanding of online sandboxes and to more efficient malware analysis using online sandboxes.

We propose a design and implementation for an Application Programming Interface (API) monitoring system called API Chaser, which is resistant to evasion-type anti-analysis techniques, e.g., stolen code and code injection. The core technique in API Chaser is code tainting, which enables us to identify precisely the execution of monitored instructions by propagating three types of taint tags added to the codes of API, malware, and benign executables, respectively. Additionally, we introduce taint-based control transfer interception, which is a technique to capture precisely API calls invoked from evasive malware. We evaluate API Chaser based on several real-world and synthetic malware to demonstrate the accuracy of our API hooking technique. We also perform a large-scale malware experiment by analyzing 8, 897 malware samples to show the practical capability of API Chaser. These experimental results show that 701 out of 8, 897 malware samples employ hook evasion techniques to hide specific API calls, while 344 malware ones use target evasion techniques to hide the source of API calls.