FraudDroid: Automated Ad Fraud Detection for Android Apps

Although mobile ad frauds have been widespread, state-of-the-art approaches in the literature have mainly focused on detecting the so-called static placement frauds, where only a single UI state is involved and can be identified based on static information such as the size or location of ad views. Other types of fraud exist that involve multiple UI states and are performed dynamically while users interact with the app. Such dynamic interaction frauds, although now widely spread in apps, have not yet been explored nor addressed in the literature. In this work, we investigate a wide range of mobile ad frauds to provide a comprehensive taxonomy to the research community. We then propose, FraudDroid, a novel hybrid approach to detect ad frauds in mobile Android apps. FraudDroid analyses apps dynamically to build UI state transition graphs and collects their associated runtime network traffics, which are then leveraged to check against a set of heuristic-based rules for identifying ad fraudulent behaviours. We show empirically that FraudDroid detects ad frauds with a high precision (93%) and recall (92%). Experimental results further show that FraudDroid is capable of detecting ad frauds across the spectrum of fraud types. By analysing 12,000 ad-supported Android apps, FraudDroid identified 335 cases of fraud associated with 20 ad networks that are further confirmed to be true positive results and are shared with our fellow researchers to promote advanced ad fraud detectionComment: 12 pages, 10 figure

Enhancing Automated GUI Exploration Techniques for Android Mobile Applications

Mobile software applications ("apps") are used by billions of smartphone owners worldwide. The demand for quality to these apps has grown together with their spread. Therefore, effective techniques and tools are being requested to support developers in mobile app quality engineering activities. Automation tools can facilitate these activities since they can save humans from routine, time consuming and error prone manual tasks. Automated GUI exploration techniques are widely adopted by researchers and practitioners in the context of mobile apps for supporting critical engineering tasks such as reverse engineering, testing, and network traffic signature generation. These techniques iteratively exercise a running app by exploiting the information that the app exposes at runtime through its GUI to derive the set of input events to be fired. Although several automated GUI exploration techniques have been proposed in the literature, they suffer from some limitations that may hinder them from a thorough app exploration. This dissertation proposes two novel solutions that contribute to the literature in Software Engineering towards improving existing automated GUI exploration techniques for mobile software applications. The former is a fully automated GUI exploration technique that aims to detect issues tied to the app instances lifecycle, a mobile-specific feature that allows users to smoothly navigate through an app and switch between apps. In particular, this technique addresses the issues of crashes and GUI failures, that consists in the manifestation of unexpected GUI states. This work includes two exploratory studies that prove that GUI failures are a widespread problem in the context of mobile apps. The latter solution is a hybrid exploration technique that combines automated GUI exploration with capture and replay through machine learning. It exploits app-specific knowledge that only human users can provide in order to explore relevant parts of the application that can be reached only by firing complex sequences of input events on specific GUIs and by choosing specific input values. Both the techniques have been implemented in tools that target the Android Operating System, that is today the world’s most popular mobile operating system. The effectiveness of the proposed techniques is demonstrated through experimental evaluations performed on real mobile apps

Policy Enforcement with Proactive Libraries

Software libraries implement APIs that deliver reusable functionalities. To correctly use these functionalities, software applications must satisfy certain correctness policies, for instance policies about the order some API methods can be invoked and about the values that can be used for the parameters. If these policies are violated, applications may produce misbehaviors and failures at runtime. Although this problem is general, applications that incorrectly use API methods are more frequent in certain contexts. For instance, Android provides a rich and rapidly evolving set of APIs that might be used incorrectly by app developers who often implement and publish faulty apps in the marketplaces. To mitigate this problem, we introduce the novel notion of proactive library, which augments classic libraries with the capability of proactively detecting and healing misuses at run- time. Proactive libraries blend libraries with multiple proactive modules that collect data, check the correctness policies of the libraries, and heal executions as soon as the violation of a correctness policy is detected. The proactive modules can be activated or deactivated at runtime by the users and can be implemented without requiring any change to the original library and any knowledge about the applications that may use the library. We evaluated proactive libraries in the context of the Android ecosystem. Results show that proactive libraries can automati- cally overcome several problems related to bad resource usage at the cost of a small overhead.Comment: O. Riganelli, D. Micucci and L. Mariani, "Policy Enforcement with Proactive Libraries" 2017 IEEE/ACM 12th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS), Buenos Aires, Argentina, 2017, pp. 182-19

A Hybrid Approach for Reverse Engineering GUI Model from Android Apps for Automated Testing

Nowadays, smartphone users are increasingly relying on mobile applications to complete most of their daily tasks. As such, mobile applications are becoming more and more complex. Therefore, software testers can no longer rely on manual testing methods to test mobile applications. Automated model-based testing techniques are recently used to test mobile applications. However, the models generated by existing techniques are of insufficient quality. This paper proposed a hybrid technique for reverse engineering graphical user interface (GUI) model from mobile applications. It performs static analysis of application’s bytecode to extract GUI information followed by a dynamic crawling to systematically explore and reverse engineer a model of the application under test. A case study was performed on real-world mobile apps to evaluate the effectiveness of the technique. The results showed that the proposed technique can generate a model with high coverage of mobile apps behaviour

Deep Reinforcement Learning Driven Applications Testing

Applications have become indispensable in our lives, and ensuring their correctness is now a critical issue. Automatic system test case generation can significantly improve the testing process for these applications, which has recently motivated researchers to work on this problem, defining various approaches. However, most state-of-the-art approaches automatically generate test cases leveraging symbolic execution or random exploration techniques. This led to techniques that lose efficiency when dealing with an increasing number of program constraints and become inapplicable when conditions are too challenging to solve or even to formulate. This Ph.D. thesis proposes addressing current techniques' limitations by exploiting Deep Reinforcement Learning. Deep Reinforcement Learning (Deep RL) is a machine learning technique that does not require a labeled training set as input since the learning process is guided by the positive or negative reward experienced during the tentative execution of a task. Hence, it can be used to dynamically learn how to build a test suite based on the feedback obtained during past successful or unsuccessful attempts. This dissertation presents three novel techniques that exploit this intuition: ARES, RONIN, and IFRIT. Since functional testing and security testing are complementary, this Ph.D. thesis explores both testing techniques using the same approach for test cases generation. ARES is a Deep RL approach for functional testing of Android apps. RONIN addresses the issue of generating exploits for a subset of Android ICC vulnerabilities. Subsequently, to better expose the bugs discovered by previous techniques, this thesis presents IFRIT, a focused testing approach capable of increasing the number of test cases that can reach a specific target (i.e., a precise section or statement of an application) and their diversity. IFRIT has the ultimate goal of exposing faults affecting the given program point

JITANA: A modern hybrid program analysis framework for android platforms

Security vetting of Android apps is often performed under tight time constraints (e.g., a few minutes). As such, vetting activities must be performed “at speed”, when an app is submitted for distribution or a device is analyzed for malware. Existing static and dynamic program analysis approaches are not feasible for use in security analysis tools because they require a much longer time to operate than security analysts can afford. There are two factors that limit the performance and efficiency of current analysis approaches. First, existing approaches analyze only one app at a time. Finding security vulnerabilities in collaborative environments such as Android, however, requires collaborating apps to be analyzed simultaneously. Thus, existing approaches are not adequate when applied in this context. Second, existing static program analysis approaches tend to operate in a “closed world” fashion; therefore, they are not easily integrated with dynamic analysis processes to efficiently produce hybrid analysis results within a given time constraint. In this work, we introduce JITANA, an efficient and scalable hybrid program analysis framework for Android. JITANA has been designed from the ground up to be used as a building block to construct efficient and scalable program analysis techniques. JITANA also operates in an open world fashion, so malicious code detected as part of dynamic analysis can be quickly analyzed and the analysis results can be seamlessly integrated with the original static analysis results. To illustrate JITANA’s capability, we used it to analyze a large collection of apps simultaneously to identify potential collaborations among apps. We have also constructed several analysis techniques on top of JITANA and we use these to perform security vetting under four realistic scenarios. The results indicate that JITANA is scalable and robust; it can effectively and efficiently analyze complex apps including Facebook, Pokémon Go, and Pandora that the state-of-the-art approach cannot handle. In addition, we constructed a visualization engine as a plugin for JITANA to provide real-time feedback on code coverage to help analysts assess their vetting efforts. Such feedback can lead analysts to hard to reach code segments that may need further analysis. Finally we illustrate the effectiveness of JITANA in detecting and analyzing dynamically loaded code. Supplementary material attached below

The Evolution of Android Malware and Android Analysis Techniques

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Web access monitoring mechanism via Android WebView for threat analysis

Many Android apps employ WebView, a component that enables the display of web content in the apps without redirecting users to web browser apps. However, WebView might also be used for cyberattacks. Moreover, to the best of our knowledge, although some countermeasures based on access control have been reported for attacks exploiting WebView, no mechanism for monitoring web access via WebView has been proposed and no analysis results focusing on web access via WebView are available. In consideration of this limitation, we propose a web access monitoring mechanism for Android WebView to analyze web access via WebView and clarify attacks exploiting WebView. In this paper, we present the design and implementation of this mechanism by modifying Chromium WebView without any modifications to the Android framework or Linux kernel. The evaluation results of the performance achieved on introducing the proposed mechanism are also presented here. Moreover, the result of threat analysis of displaying a fake virus alert while browsing websites on Android is discussed to demonstrate the effectiveness of the proposed mechanism

Program Analysis Based Approaches to Ensure Security and Safety of Emerging Software Platforms

Our smartphones, homes, hospitals, and automobiles are being enhanced with software that provide an unprecedentedly rich set of functionalities, which has created an enormous market for the development of software that run on almost every personal computing devices in a person's daily life, including security- and safety-critical ones. However, the software development support provided by the emerging platforms also raises security risks by allowing untrusted third-party code, which can potentially be buggy, vulnerable or even malicious to control user's device. Moreover, as the Internet-of-Things (IoT) technology is gaining vast adoptions by a wide range of industries, and is penetrating every aspects of people's life, safety risks brought by the open software development support of the emerging IoT platform (e.g., smart home) could bring more severe threat to the well-being of customers than what security vulnerabilities in mobile apps have done to a cell phone user. To address this challenge posed on the software security in emerging domains, my dissertation focuses on the flaws, vulnerabilities and malice in the software developed for platforms in these domains. Specifically, we demonstrate that systematic program analyses of software (1) Lead to an understanding of design and implementation flaws across different platforms that can be leveraged in miscellaneous attacks or causing safety problems; (2) Lead to the development of security mechanisms that limit the potential for these threats.We contribute static and dynamic program analysis techniques for three modern platforms in emerging domains -- smartphone, smart home, and autonomous vehicle. Our app analysis reveals various different vulnerabilities and design flaws on these platforms, and we propose (1) static analysis tool OPAnalyzer to automates the discovery of problems by searching for vulnerable code patterns; (2) dynamic testing tool AutoFuzzer to efficiently produce and capture domain specific issues that are previously undefined; and (3) propose new access control mechanism ContexIoT to strengthen the platform's immunity to the vulnerability and malice in third-party software. Concretely, we first study a vulnerability family caused by the open ports on mobile devices, which allows remote exploitation due to insufficient protection. We devise a tool called OPAnalyzer to perform the first systematic study of open port usage and their security implications on mobile platform, which effectively identify and characterize vulnerable open port usage at scale in popular Android apps. We further identify the lack of context-based access control as a main enabler for such attacks, and begin to seek for defense solution to strengthen the system security. We study the popular smart home platform, and find the existing access control mechanisms to be coarse-grand, insufficient, and undemanding. Taking lessons from previous permission systems, we propose the ContexIoT approach, a context-based permission system for IoT platform that supports third-party app development, which protects the user from vulnerability and malice in these apps through fine-grained identification of context. Finally, we design dynamic fuzzing tool, AutoFuzzer for the testing of self-driving functionalities, which demand very high code quality using improved testing practice combining the state-of-the-art fuzzing techniques with vehicular domain knowledge, and discover problems that lead to crashes in safety-critical software on emerging autonomous vehicle platform.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145845/1/jackjia_1.pd