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We describe a set of artifacts for dynamic analysis of Android apps, including a dataset used in a dynamic characterization study, source code used for performing the study, an Android inter-app benchmark suite, and definition of Android behavioral metrics.
Android, the most popular mobile OS, has around 78% of the mobile market share. Due to its popularity, it attracts many malware attacks. In fact, people have discovered around 1 million new malware samples per quarter, and it was reported that over 98% of these new malware samples are in fact “derivatives” (or variants) from existing malware families. In this paper, we first show that runtime behaviors...
Attackers have been searching for security vulnerabilities in Android applications to exploit. One of these security vulnerabilities is that Android applications could load codes at runtime. This helps attackers to avoid being detected by static analysis tools. In this study, we have done a rigorous analysis to see how attackers employ updating techniques in order to exploit this vulnerability, and...
This paper proposes an Android malware detection approach based on attack tree. Attack tree model is extended to provide a novel way to organize and exploit behavior rules. Connections between attack goals and application capability are represented by an attack tree structure and behavior rules are assigned to every attack path in the attack tree. In this way, fine-grained and comprehensive static...
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