Mapping System Level Behaviors with Android APIs via System Call Dependence Graphs

Due to Android's open source feature and low barriers to entry for developers, millions of developers and third-party organizations have been attracted into the Android ecosystem. However, over 90 percent of mobile malware are found targeted on Android. Though Android provides multiple security features and layers to protect user data and system resources, there are still some over-privileged applications in Google Play Store or third-party Android app stores at wild. In this paper, we proposed an approach to map system level behavior and Android APIs, based on the observation that system level behaviors cannot be avoided but sensitive Android APIs could be evaded. To the best of our knowledge, our approach provides the first work to map system level behavior and Android APIs through System Call Dependence Graphs. The study also shows that our approach can effectively identify potential permission abusing, with almost negligible performance impact.Comment: 14 pages, 6 figure

An Empirical Study on Android for Saving Non-shared Data on Public Storage

With millions of apps that can be downloaded from official or third-party market, Android has become one of the most popular mobile platforms today. These apps help people in all kinds of ways and thus have access to lots of user's data that in general fall into three categories: sensitive data, data to be shared with other apps, and non-sensitive data not to be shared with others. For the first and second type of data, Android has provided very good storage models: an app's private sensitive data are saved to its private folder that can only be access by the app itself, and the data to be shared are saved to public storage (either the external SD card or the emulated SD card area on internal FLASH memory). But for the last type, i.e., an app's non-sensitive and non-shared data, there is a big problem in Android's current storage model which essentially encourages an app to save its non-sensitive data to shared public storage that can be accessed by other apps. At first glance, it seems no problem to do so, as those data are non-sensitive after all, but it implicitly assumes that app developers could correctly identify all sensitive data and prevent all possible information leakage from private-but-non-sensitive data. In this paper, we will demonstrate that this is an invalid assumption with a thorough survey on information leaks of those apps that had followed Android's recommended storage model for non-sensitive data. Our studies showed that highly sensitive information from billions of users can be easily hacked by exploiting the mentioned problematic storage model. Although our empirical studies are based on a limited set of apps, the identified problems are never isolated or accidental bugs of those apps being investigated. On the contrary, the problem is rooted from the vulnerable storage model recommended by Android. To mitigate the threat, we also propose a defense framework

Towards model checking Android applications

As feature-rich Android applications (apps for short) are increasingly popularized in security-sensitive scenarios, methods to verify their security properties are highly desirable. Existing approaches on verifying Android apps often have limited effectiveness. For instance, static analysis often suffers from a high false-positive rate, whereas approaches based on dynamic testing are limited in coverage. In this work, we propose an alternative approach, which is to apply the software model checking technique to verify Android apps. We have built a general framework named DroidPF upon Java PathFinder (JPF), towards model checking Android apps. In the framework, we craft an executable mock-up Android OS which enables JPF to dynamically explore the concrete state spaces of the tested apps; we construct programs to generate user interaction and environmental input so as to drive the dynamic execution of the apps; and we introduce Android specific reduction techniques to help alleviate the state space explosion. DroidPF focuses on common security vulnerabilities in Android apps including sensitive data leakage involving a non-trivial flow- and context-sensitive taint-style analysis. DroidPF has been evaluated with 131 apps, which include real-world apps, third-party libraries, malware samples and benchmarks for evaluating app analysis techniques like ours. DroidPF precisely identifies nearly all of the previously known security issues and nine previously unreported vulnerabilities/bugs.NRF (Natl Research Foundation, S’pore

Android security: analysis and applications

The Android mobile system is home to millions of apps that offer a wide range of functionalities. Users rely on Android apps in various facets of daily life, including critical, e.g., medical, settings. Generally, users trust that apps perform their stated purpose safely and accurately. However, despite the platform’s efforts to maintain a safe environment, apps routinely manage to evade scrutiny. This dissertation analyzes Android app behavior and has revealed several weakness: lapses in device authentication schemes, deceptive practices such as apps covering their traces, as well as behavioral and descriptive inaccuracies in medical apps. Examining a large corpus of applications has revealed that suspicious behavior is often the result of lax oversight, and can occur without an explicit intent to harm users. Nevertheless, flawed app behavior is present, and is especially problematic in apps that perform critical tasks. Additionally, manufacturer’s and app developer’s claims often do not mirror actual functionalities, e.g., as we reveal in our study of LG’s Knock Code authentication scheme, and as evidenced by the removal of Google Play medical apps due to overstated functionality claims. This dissertation makes the following contributions: (1) quantifying the security of LG’s Knock Code authentication method, (2) defining deceptive practices of self-hiding app behavior found in popular apps, (3) verifying abuses of device administrator features, (4) characterizing the medical app landscape found on Google Play, (5) detailing the claimed behaviors and conditions of medical apps using ICD codes and app descriptions, (6) verifying errors in medical score calculator app implementations, and (7) discerning how medical apps should be regulated within the jurisdiction of regulatory frameworks based on their behavior and data acquired from users

Towards Modular and Flexible Access Control on Smart Mobile Devices

Smart mobile devices, such as smartphones and tablets, have become an integral part of our daily personal and professional lives. These devices are connected to a wide variety of Internet services and host a vast amount of applications, which access, store and process security- and privacy-sensitive data. A rich set of sensors, ranging from microphones and cameras to location and acceleration sensors, allows these applications and their back end services to reason about user behavior. Further, enterprise administrators integrate smart mobile devices into their IT infrastructures to enable comfortable work on the go. Unsurprisingly, this abundance of available high-quality information has made smart mobile devices an interesting target for attackers, and the number of malicious and privacy-intrusive applications has steadily been rising. Detection and mitigation of such malicious behavior are in focus of mobile security research today. In particular, the Android operating system has received special attention by both academia and industry due to its popularity and open-source character. Related work has scrutinized its security architecture, analyzed attack vectors and vulnerabilities and proposed a wide variety of security extensions. While these extensions have diverse goals, many of them constitute modifications of the Android operating system and extend its default permission-based access control model. However, they are not generic and only address specific security and privacy concerns. The goal of this dissertation is to provide generic and extensible system-centric access control architectures, which can serve as a solid foundation for the instantiation of use-case specific security extensions. In doing so, we enable security researchers, enterprise administrators and end users to design, deploy and distribute security extensions without further modification of the underlying operating system. To achieve this goal, we first analyze the mobile device ecosystem and discuss how Android's security architecture aims to address its inherent threats. We proceed to survey related work on Android security, focusing on system-centric security extensions, and derive a set of generic requirements for extensible access control architectures targeting smart mobile devices. We then present two extensible access control architectures, which address these requirements by providing policy-based and programmable interfaces for the instantiation of use-case specific security solutions. By implementing a set of practical use-cases, ranging from context-aware access control, dynamic application behavior analysis to isolation of security domains we demonstrate the advantages of system-centric access control architectures over application-layer approaches. Finally, we conclude this dissertation by discussing an alternative approach, which is based on application-layer deputies and can be deployed whenever practical limitations prohibit the deployment of system-centric solutions

Mechanisms for Resource Protection on the Android Platform

Ph.DDOCTOR OF PHILOSOPH

Android Application Security Scanning Process

This chapter presents the security scanning process for Android applications. The aim is to guide researchers and developers to the core phases/steps required to analyze Android applications, check their trustworthiness, and protect Android users and their devices from being victims to different malware attacks. The scanning process is comprehensive, explaining the main phases and how they are conducted including (a) the download of the apps themselves; (b) Android application package (APK) reverse engineering; (c) app feature extraction, considering both static and dynamic analysis; (d) dataset creation and/or utilization; and (e) data analysis and data mining that result in producing detection systems, classification systems, and ranking systems. Furthermore, this chapter highlights the app features, evaluation metrics, mechanisms and tools, and datasets that are frequently used during the app’s security scanning process

Towards a Network-based Approach for Smartphone Security

Smartphones have become an important utility that affects many aspects of our daily life. Due to their large dissemination and the tasks that are performed with them, they have also become a valuable target for criminals. Their specific capabilities and the way they are used introduce new threats in terms of information security. The research field of smartphone security has gained a lot of momentum in the past eight years. Approaches that have been presented so far focus on investigating design flaws of smartphone operating systems as well as their potential misuse by an adversary. Countermeasures are often realized based upon extensions made to the operating system itself, following a host-based design approach. However, there is a lack of network-based mechanisms that allow a secure integration of smartphones into existing IT infrastructures. This topic is especially relevant for companies whose employees use smartphones for business tasks. This thesis presents a novel, network-based approach for smartphone security called CADS: Context-related Signature and Anomaly Detection for Smartphones. It allows to determine the security status of smartphones by analyzing three aspects: (1) their current configuration in terms of installed software and available hardware, (2) their behavior and (3) the context they are currently used in. Depending on the determined security status, enforcement actions can be defined in order to allow or to deny access to services provided by the respective IT infrastructure. The approach is based upon the distributed collection and central analysis of data about smartphones. In contrast to other approaches, it explicitly supports to leverage existing security services both for analysis and enforcement purposes. A proof of concept is implemented based upon the IF-MAP protocol for network security and the Google Android platform. An evaluation verifies (1) that the CADS approach is able to detect so-called sensor sniffing attacks and (2) that reactions can be triggered based on detection results to counter ongoing attacks. Furthermore, it is demonstrated that the functionality of an existing, host-based approach that relies on modifications of the Android smartphone platform can be mimicked by the CADS approach. The advantage of CADS is that it does not need any modifications of the Android platform itself