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Runze Tan

Publications and source records attributed to Runze Tan.

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Assessing the Capability of Android Dynamic Analysis Tools to Combat Anti-Runtime Analysis Techniques

As the dominant mobile operating system, Android continues to attract a substantial influx of new applications each year. However, this growth is accompanied by increased attention from malicious actors, resulting in a significant rise in security threats to the Android ecosystem. Among these threats, the adoption of Anti-Runtime Analysis (ARA) techniques by malicious applications poses a serious challenge, as it hinders security professionals from effectively analyzing malicious behaviors using dynamic analysis tools. ARA technologies are designed to prevent the dynamic examination of applications, thus complicating efforts to ensure platform security. This paper presents a comprehensive empirical study that assesses the ability of widely-used Android dynamic analysis tools to bypass various ARA techniques. Our findings reveal a critical gap in the effectiveness of existing dynamic analysis tools to counter ARA mechanisms, highlighting an urgent need for more robust solutions. This work provides valuable insights into the limitations of existing tools and highlights the need for improved methods to counteract ARA technologies, thus advancing the field of software security and dynamic analysis.

cs.SE

ARAP: Demystifying Anti Runtime Analysis Code in Android Apps

With the continuous growth in the usage of Android apps, ensuring their security has become critically important. An increasing number of malicious apps adopt anti-analysis techniques to evade security measures. Although some research has started to consider anti-runtime analysis (ARA), it is unfortunate that they have not systematically examined ARA techniques. Furthermore, the rapid evolution of ARA technology exacerbates the issue, leading to increasingly inaccurate analysis results. To effectively analyze Android apps, understanding their adopted ARA techniques is necessary. However, no systematic investigation has been conducted thus far. In this paper, we conduct the first systematic study of the ARA implementations in a wide range of 117,171 Android apps (including both malicious and benign ones) collected between 2016 and 2023. Additionally, we propose a specific investigation tool named ARAP to assist this study by leveraging both static and dynamic analysis. According to the evaluation results, ARAP not only effectively identifies the ARA implementations in Android apps but also reveals many important findings. For instance, almost all apps have implemented at least one category of ARA technology (99.6% for benign apps and 97.0% for malicious apps).

cs.CR