Smartphone apps usually have access to sensitive user data such as contacts, geo-location, and account credentials and they might share such data to external entities through the Internet or with other apps. Confidentiality of user data could be breached if there are anomalies in the way sensitive data is handled by an app which is vulnerable or malicious. Existing approaches that detect anomalous sensitive data flows have limitations in terms of accuracy because the definition of anomalous flows may differ for different apps with different functionalities; it is normal for "Health" apps to share heart rate information through the Internet but is anomalous for "Travel" apps. In this paper, we propose a novel approach to detect anomalous sensitive data flows in Android apps, with improved accuracy. To achieve this objective, we first group trusted apps according to the topics inferred from their functional descriptions. We then learn sensitive information flows with respect to each group of trusted apps. For a given app under analysis, anomalies are identified by comparing sensitive information flows in the app against those flows learned from trusted apps grouped under the same topic. In the evaluation, information flow is learned from 11,796 trusted apps. We then checked for anomalies in 596 new (benign) apps and identified 2 previously-unknown vulnerable apps related to anomalous flows. We also analyzed 18 malware apps and found anomalies in 6 of them.
Other closely related work is about detecting permission re-delegation vulnerabilities in apps. @cite_23 presented the permission re-delegation problems, and their approach detects them whenever there exists a path from a public entry point to a privileged API call. @cite_14 and @cite_12 also detect permission re-delegation vulnerabilities. However, as acknowledged by and , their approaches cannot differentiate between legitimate and illegitimate permission re-delegation behaviors.
@cite_14 @cite_12 @cite_23
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Modern browsers and smartphone operating systems treat applications as mutually untrusting, potentially malicious principals. Applications are (1) isolated except for explicit IPC or inter-application communication channels and (2) unprivileged by default, requiring user permission for additional privileges. Although inter-application communication supports useful collaboration, it also introduces the risk of permission redelegation. Permission re-delegation occurs when an application with permissions performs a privileged task for an application without permissions. This undermines the requirement that the user approve each application's access to privileged devices and data. We discuss permission re-delegation and demonstrate its risk by launching real-world attacks on Android system applications; several of the vulnerabilities have been confirmed as bugs. We discuss possible ways to address permission redelegation and present IPC Inspection, a new OS mechanism for defending against permission re-delegation. IPC Inspection prevents opportunities for permission redelegation by reducing an application's permissions after it receives communication from a less privileged application. We have implemented IPC Inspection for a browser and Android, and we show that it prevents the attacks we found in the Android system applications. Android provides third-party applications with an extensive API that includes access to phone hardware, settings, and user data. Access to privacy- and security-relevant parts of the API is controlled with an install-time application permission system. We study Android applications to determine whether Android developers follow least privilege with their permission requests. We built Stowaway, a tool that detects overprivilege in compiled Android applications. Stowaway determines the set of API calls that an application uses and then maps those API calls to permissions. We used automated testing tools on the Android API in order to build the permission map that is necessary for detecting overprivilege. We apply Stowaway to a set of 940 applications and find that about one-third are overprivileged. We investigate the causes of overprivilege and find evidence that developers are trying to follow least privilege but sometimes fail due to insufficient API documentation. The Google Android mobile phone platform is one of the most anticipated smartphone operating systems on the market. The open source Android platform allows developers to take full advantage of the mobile operation system, but also raises significant issues related to malicious applications. On one hand, the popularity of Android absorbs attention of most developers for producing their applications on this platform. The increased numbers of applications, on the other hand, prepares a suitable prone for some users to develop different kinds of malware and insert them in Google Android market or other third party markets as safe applications. In this paper, we propose to combine permission and API (Application Program Interface) calls and use machine learning methods to detect malicious Android Apps. In our design, the permission is extracted from each App's profile information and the APIs are extracted from the packed App file by using packages and classes to represent API calls. By using permissions and API calls as features to characterize each Apps, we can learn a classifier to identify whether an App is potentially malicious or not. An inherent advantage of our method is that it does not need to involve any dynamical tracing of the system calls but only uses simple static analysis to find system functions involved in each App. In addition, because permission settings and APIs are alwaysavailable for each App, our method can be generalized to all mobile applications. Experiments on real-world Apps with more than 1200 malware and 1200 benign samples validate the algorithm performance. Recently, the threat of Android malware is spreading rapidly, especially those repackaged Android malware. Although understanding Android malware using dynamic analysis can provide a comprehensive view, it is still subjected to high cost in environment deployment and manual efforts in investigation. In this study, we propose a static feature-based mechanism to provide a static analyst paradigm for detecting the Android malware. The mechanism considers the static information including permissions, deployment of components, Intent messages passing and API calls for characterizing the Android applications behavior. In order to recognize different intentions of Android malware, different kinds of clustering algorithms can be applied to enhance the malware modeling capability. Besides, we leverage the proposed mechanism and develop a system, called Droid Mat. First, the Droid Mat extracts the information (e.g., requested permissions, Intent messages passing, etc) from each applicationi¦s manifest file, and regards components (Activity, Service, Receiver) as entry points drilling down for tracing API Calls related to permissions. Next, it applies K-means algorithm that enhances the malware modeling capability. The number of clusters are decided by Singular Value Decomposition (SVD) method on the low rank approximation. Finally, it uses kNN algorithm to classify the application as benign or malicious. The experiment result shows that the recall rate of our approach is better than one of well-known tool, Androguard, published in Black hat 2011, which focuses on Android malware analysis. In addition, Droid Mat is efficient since it takes only half of time than Androguard to predict 1738 apps as benign apps or Android malware.