Biblio

While there have been various studies identifying and classifying Android malware, there is limited discussion of the broader class of apps that fall in a gray area. Mobile grayware is distinct from PC grayware due to differences in operating system properties. Due to mobile grayware’s subjective nature, it is difficult to identify mobile grayware via program analysis alone. Instead, we hypothesize enhancing analysis with text analytics can effectively reduce human effort when triaging grayware. In this paper, we design and implement heuristics for seven main categories of grayware.We then use these heuristics to simulate grayware triage on a large set of apps from Google Play. We then present the results of our empirical study, demonstrating a clear problem of grayware. In doing so, we show how even relatively simple heuristics can quickly triage apps that take advantage of users in an undesirable way.
 

Mobile malware attempts to evade detection during app analysis by mimicking security-sensitive behaviors of benign apps that provide similar functionality (e.g., sending SMS mes- sages), and suppressing their payload to reduce the chance of being observed (e.g., executing only its payload at night). Since current approaches focus their analyses on the types of security- sensitive resources being accessed (e.g., network), these evasive techniques in malware make differentiating between malicious and benign app behaviors a difficult task during app analysis. We propose that the malicious and benign behaviors within apps can be differentiated based on the contexts that trigger security- sensitive behaviors, i.e., the events and conditions that cause the security-sensitive behaviors to occur. In this work, we introduce AppContext, an approach of static program analysis that extracts the contexts of security-sensitive behaviors to assist app analysis in differentiating between malicious and benign behaviors. We implement a prototype of AppContext and evaluate AppContext on 202 malicious apps from various malware datasets, and 633 benign apps from the Google Play Store. AppContext correctly identifies 192 malicious apps with 87.7% precision and 95% recall. Our evaluation results suggest that the maliciousness of a security-sensitive behavior is more closely related to the intention of the behavior (reflected via contexts) than the type of the security-sensitive resources that the behavior accesses.