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.
@cite_10 proposed Appsealer, a runtime patch to mitigate permission re-delegation problem. They perform static data flow analysis to determine sensitive data flows from sources to sinks and apply a patch before the invocations of privileged APIs such that the app alerts the user of potential permission re-delegation attacks and requests the user's authorization to continue. This is an alternative way of distinguishing behaviors and abnormal ones by relying on the user. @cite_6 also proposed a similar approach but they extended the Android framework to track ICC vulnerabilities instead of patching the app. Instead of relying on the user, who might not be aware of security implications, we resort to a model that reflects normal information flow behaviors to detect anomalies in the flow of sensitive information.
@cite_10 @cite_6
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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. Bugs in the authorisation logic of web applications can expose the data of one user to another. Such data disclosure vulnerabilities are common---they can be caused by a single omitted access control check in the application. We make the observation that, while the implementation of the authorisation logic is complex and therefore error-prone, most web applications only use simple access control models, in which each piece of data is accessible by a user or a group of users. This makes it possible to validate the correct operation of the authorisation logic externally, based on the observed data in HTTP traffic to and from an application. We describe FlowWatcher, an HTTP proxy that mitigates data disclosure vulnerabilities in unmodified web applications. FlowWatcher monitors HTTP traffic and shadows part of an application's access control state based on a rule-based specification of the user-data-access (UDA) policy. The UDA policy states the intended data ownership and how it changes based on observed HTTP requests. FlowWatcher detects violations of the UDA policy by tracking data items that are likely to be unique across HTTP requests and responses of different users. We evaluate a prototype implementation of FlowWatcher as a plug-in for the Nginx reverse proxy and show that, with short UDA policies, it can mitigate CVE bugs in six popular web applications. Android phones often carry personal information, attracting malicious developers to embed code in Android applications to steal sensitive data. With known techniques in the literature, one may easily determine if sensitive data is being transmitted out of an Android phone. However, transmission of sensitive data in itself does not necessarily indicate privacy leakage; a better indicator may be whether the transmission is by user intention or not. When transmission is not intended by the user, it is more likely a privacy leakage. The problem is how to determine if transmission is user intended. As a first solution in this space, we present a new analysis framework called AppIntent. For each data transmission, AppIntent can efficiently provide a sequence of GUI manipulations corresponding to the sequence of events that lead to the data transmission, thus helping an analyst to determine if the data transmission is user intended or not. The basic idea is to use symbolic execution to generate the aforementioned event sequence, but straightforward symbolic execution proves to be too time-consuming to be practical. A major innovation in AppIntent is to leverage the unique Android execution model to reduce the search space without sacrificing code coverage. We also present an evaluation of AppIntent with a set of 750 malicious apps, as well as 1,000 top free apps from Google Play. The results show that AppIntent can effectively help separate the apps that truly leak user privacy from those that do not. Today's smartphones are a ubiquitous source of private and confidential data. At the same time, smartphone users are plagued by carelessly programmed apps that leak important data by accident, and by malicious apps that exploit their given privileges to copy such data intentionally. While existing static taint-analysis approaches have the potential of detecting such data leaks ahead of time, all approaches for Android use a number of coarse-grain approximations that can yield high numbers of missed leaks and false alarms. In this work we thus present FlowDroid, a novel and highly precise static taint analysis for Android applications. A precise model of Android's lifecycle allows the analysis to properly handle callbacks invoked by the Android framework, while context, flow, field and object-sensitivity allows the analysis to reduce the number of false alarms. Novel on-demand algorithms help FlowDroid maintain high efficiency and precision at the same time. We also propose DroidBench, an open test suite for evaluating the effectiveness and accuracy of taint-analysis tools specifically for Android apps. As we show through a set of experiments using SecuriBench Micro, DroidBench, and a set of well-known Android test applications, FlowDroid finds a very high fraction of data leaks while keeping the rate of false positives low. On DroidBench, FlowDroid achieves 93 recall and 86 precision, greatly outperforming the commercial tools IBM AppScan Source and Fortify SCA. FlowDroid successfully finds leaks in a subset of 500 apps from Google Play and about 1,000 malware apps from the VirusShare project.