Improving privacy on android smartphones through in-vivo bytecode instrumentation

In this paper we claim that a widely applicable and efficient means to fight against malicious mobile Android applications is: 1) to per- form runtime monitoring 2) by instrumenting the application byte- code and 3) in-vivo, i.e. directly on the smartphone. We present a tool chain to do this and present experimental results showing that this tool chain can run on smartphones in a reasonable amount of time and with a realistic effort. Our findings also identify chal- lenges to be addressed before running powerful runtime monitoring and instrumentations directly on smartphones. We implemented two use-cases leveraging the tool chain: FineGPolicy, a fine-grained user centric permission policy system and AdRemover an adver- tisement remover. Both prototypes improve the privacy of Android systems thanks to in-vivo bytecode instrumentation

In-Vivo Bytecode Instrumentation for Improving Privacy on Android Smartphones in Uncertain Environments

In this paper we claim that an efficient and readily applicable means to improve privacy of Android applications is: 1) to perform runtime monitoring by instrumenting the application bytecode and 2) in-vivo, i.e. directly on the smartphone. We present a tool chain to do this and present experimental results showing that this tool chain can run on smartphones in a reasonable amount of time and with a realistic effort. Our findings also identify challenges to be addressed before running powerful runtime monitoring and instrumentations directly on smartphones. We implemented two use-cases leveraging the tool chain: BetterPermissions, a fine-grained user centric permission policy system and AdRemover an advertisement remover. Both prototypes improve the privacy of Android systems thanks to in-vivo bytecode instrumentation.Comment: ISBN: 978-2-87971-111-

I know what leaked in your pocket: uncovering privacy leaks on Android Apps with Static Taint Analysis

Android applications may leak privacy data carelessly or maliciously. In this work we perform inter-component data-flow analysis to detect privacy leaks between components of Android applications. Unlike all current approaches, our tool, called IccTA, propagates the context between the components, which improves the precision of the analysis. IccTA outperforms all other available tools by reaching a precision of 95.0% and a recall of 82.6% on DroidBench. Our approach detects 147 inter-component based privacy leaks in 14 applications in a set of 3000 real-world applications with a precision of 88.4%. With the help of ApkCombiner, our approach is able to detect inter-app based privacy leaks

Malware Analysis and Privacy Policy Enforcement Techniques for Android Applications

The rapid increase in mobile malware and deployment of over-privileged applications over the years has been of great concern to the security community. Encroaching on user’s privacy, mobile applications (apps) increasingly exploit various sensitive data on mobile devices. The information gathered by these applications is sufficient to uniquely and accurately profile users and can cause tremendous personal and financial damage. On Android specifically, the security and privacy holes in the operating system and framework code has created a whole new dynamic for malware and privacy exploitation. This research work seeks to develop novel analysis techniques that monitor Android applications for possible unwanted behaviors and then suggest various ways to deal with the privacy leaks associated with them. Current state-of-the-art static malware analysis techniques on Android-focused mainly on detecting known variants without factoring any kind of software obfuscation. The dynamic analysis systems, on the other hand, are heavily dependent on extending the Android OS and/or runtime virtual machine. These methodologies often tied the system to a single Android version and/or kernel making it very difficult to port to a new device. In privacy, accesses to the database system’s objects are not controlled by any security check beyond overly-broad read/write permissions. This flawed model exposes the database contents to abuse by privacy-agnostic apps and malware. This research addresses the problems above in three ways. First, we developed a novel static analysis technique that fingerprints known malware based on three-level similarity matching. It scores similarity as a function of normalized opcode sequences found in sensitive functional modules and application permission requests. Our system has an improved detection ratio over current research tools and top COTS anti-virus products while maintaining a high level of resiliency to both simple and complex obfuscation. Next, we augment the signature-related weaknesses of our static classifier with a hybrid analysis system which incorporates bytecode instrumentation and dynamic runtime monitoring to examine unknown malware samples. Using the concept of Aspect-oriented programming, this technique involves recompiling security checking code into an unknown binary for data flow analysis, resource abuse tracing, and analytics of other suspicious behaviors. Our system logs all the intercepted activities dynamically at runtime without the need for building custom kernels. Finally, we designed a user-level privacy policy enforcement system that gives users more control over their personal data saved in the SQLite database. Using bytecode weaving for query re-writing and enforcing access control, our system forces new policies at the schema, column, and entity levels of databases without rooting or voiding device warranty

Analyzing the Unanalyzable: an Application to Android Apps

In general, software is unreliable. Its behavior can deviate from users’ expectations because of bugs, vulnerabilities, or even malicious code. Manually vetting software is a challenging, tedious, and highly-costly task that does not scale. To alleviate excessive costs and analysts’ burdens, automated static analysis techniques have been proposed by both the research and practitioner communities making static analysis a central topic in software engineering. In the meantime, mobile apps have considerably grown in importance. Today, most humans carry software in their pockets, with the Android operating system leading the market. Millions of apps have been proposed to the public so far, targeting a wide range of activities such as games, health, banking, GPS, etc. Hence, Android apps collect and manipulate a considerable amount of sensitive information, which puts users’ security and privacy at risk. Consequently, it is paramount to ensure that apps distributed through public channels (e.g., the Google Play) are free from malicious code. Hence, the research and practitioner communities have put much effort into devising new automated techniques to vet Android apps against malicious activities over the last decade. Analyzing Android apps is, however, challenging. On the one hand, the Android framework proposes constructs that can be used to evade dynamic analysis by triggering the malicious code only under certain circumstances, e.g., if the device is not an emulator and is currently connected to power. Hence, dynamic analyses can -easily- be fooled by malicious developers by making some code fragments difficult to reach. On the other hand, static analyses are challenged by Android-specific constructs that limit the coverage of off-the-shell static analyzers. The research community has already addressed some of these constructs, including inter-component communication or lifecycle methods. However, other constructs, such as implicit calls (i.e., when the Android framework asynchronously triggers a method in the app code), make some app code fragments unreachable to the static analyzers, while these fragments are executed when the app is run. Altogether, many apps’ code parts are unanalyzable: they are either not reachable by dynamic analyses or not covered by static analyzers. In this manuscript, we describe our contributions to the research effort from two angles: ① statically detecting malicious code that is difficult to access to dynamic analyzers because they are triggered under specific circumstances; and ② statically analyzing code not accessible to existing static analyzers to improve the comprehensiveness of app analyses. More precisely, in Part I, we first present a replication study of a state-of-the-art static logic bomb detector to better show its limitations. We then introduce a novel hybrid approach for detecting suspicious hidden sensitive operations towards triaging logic bombs. We finally detail the construction of a dataset of Android apps automatically infected with logic bombs. In Part II, we present our work to improve the comprehensiveness of Android apps’ static analysis. More specifically, we first show how we contributed to account for atypical inter-component communication in Android apps. Then, we present a novel approach to unify both the bytecode and native in Android apps to account for the multi-language trend in app development. Finally, we present our work to resolve conditional implicit calls in Android apps to improve static and dynamic analyzers

Plugging in trust and privacy : three systems to improve widely used ecosystems

The era of touch-enabled mobile devices has fundamentally changed our communication habits. Their high usability and unlimited data plans provide the means to communicate any place, any time and lead people to publish more and more (sensitive) information. Moreover, the success of mobile devices also led to the introduction of new functionality that crucially relies on sensitive data (e.g., location-based services). With our today’s mobile devices, the Internet has become the prime source for information (e.g., news) and people need to rely on the correctness of information provided on the Internet. However, most of the involved systems are neither prepared to provide robust privacy guarantees for the users, nor do they provide users with the means to verify and trust in delivered content. This dissertation introduces three novel trust and privacy mechanisms that overcome the current situation by improving widely used ecosystems. With WebTrust we introduce a robust authenticity and integrity framework that provides users with the means to verify both the correctness and authorship of data transmitted via HTTP. X-pire! and X-pire 2.0 offer a digital expiration date for images in social networks to enforce post-publication privacy. AppGuard enables the enforcement of fine-grained privacy policies on third-party applications in Android to protect the users privacy.Heutige Mobilgeräte mit Touchscreen haben unsere Kommunikationsgewohnheiten grundlegend geändert. Ihre intuitive Benutzbarkeit gepaart mit unbegrenztem Internetzugang erlaubt es uns jederzeit und überall zu kommunizieren und führt dazu, dass immer mehr (vertrauliche) Informationen publiziert werden. Des Weiteren hat der Erfolg mobiler Geräte zur Einführung neuer Dienste die auf vertraulichen Daten aufbauen (z.B. positionsabhängige Dienste) beigetragen. Mit den aktuellen Mobilgeräten wurde zudem das Internet die wichtigste Informationsquelle (z.B. für Nachrichten) und die Nutzer müssen sich auf die Korrektheit der von dort bezogenen Daten verlassen. Allerdings bieten die involvierten Systeme weder robuste Datenschutzgarantien, noch die Möglichkeit die Korrektheit bezogener Daten zu verifizieren. Diese Dissertation führt drei neue Mechanismen für das Vertrauen und den Datenschutz ein, die die aktuelle Situation in weit verbreiteten Systemen verbessern. WebTrust, ein robustes Authentizitäts- und Integritätssystem ermöglicht es den Nutzern sowohl die Korrektheit als auch die Autorenschaft von über HTTP übertragenen Daten zu verifizieren. X-pire! und X-pire 2.0 bieten ein digitales Ablaufdatum für Bilder in sozialen Netzwerken um Daten auch nach der Publikation noch vor Zugriff durch Dritte zu schützen. AppGuard ermöglicht das Durchsetzen von feingranularen Datenschutzrichtlinien für Drittanbieteranwendungen in Android um einen angemessen Schutz der Nutzerdaten zu gewährleisten

Plugging in trust and privacy : three systems to improve widely used ecosystems

The era of touch-enabled mobile devices has fundamentally changed our communication habits. Their high usability and unlimited data plans provide the means to communicate any place, any time and lead people to publish more and more (sensitive) information. Moreover, the success of mobile devices also led to the introduction of new functionality that crucially relies on sensitive data (e.g., location-based services). With our today’s mobile devices, the Internet has become the prime source for information (e.g., news) and people need to rely on the correctness of information provided on the Internet. However, most of the involved systems are neither prepared to provide robust privacy guarantees for the users, nor do they provide users with the means to verify and trust in delivered content. This dissertation introduces three novel trust and privacy mechanisms that overcome the current situation by improving widely used ecosystems. With WebTrust we introduce a robust authenticity and integrity framework that provides users with the means to verify both the correctness and authorship of data transmitted via HTTP. X-pire! and X-pire 2.0 offer a digital expiration date for images in social networks to enforce post-publication privacy. AppGuard enables the enforcement of fine-grained privacy policies on third-party applications in Android to protect the users privacy.Heutige Mobilgeräte mit Touchscreen haben unsere Kommunikationsgewohnheiten grundlegend geändert. Ihre intuitive Benutzbarkeit gepaart mit unbegrenztem Internetzugang erlaubt es uns jederzeit und überall zu kommunizieren und führt dazu, dass immer mehr (vertrauliche) Informationen publiziert werden. Des Weiteren hat der Erfolg mobiler Geräte zur Einführung neuer Dienste die auf vertraulichen Daten aufbauen (z.B. positionsabhängige Dienste) beigetragen. Mit den aktuellen Mobilgeräten wurde zudem das Internet die wichtigste Informationsquelle (z.B. für Nachrichten) und die Nutzer müssen sich auf die Korrektheit der von dort bezogenen Daten verlassen. Allerdings bieten die involvierten Systeme weder robuste Datenschutzgarantien, noch die Möglichkeit die Korrektheit bezogener Daten zu verifizieren. Diese Dissertation führt drei neue Mechanismen für das Vertrauen und den Datenschutz ein, die die aktuelle Situation in weit verbreiteten Systemen verbessern. WebTrust, ein robustes Authentizitäts- und Integritätssystem ermöglicht es den Nutzern sowohl die Korrektheit als auch die Autorenschaft von über HTTP übertragenen Daten zu verifizieren. X-pire! und X-pire 2.0 bieten ein digitales Ablaufdatum für Bilder in sozialen Netzwerken um Daten auch nach der Publikation noch vor Zugriff durch Dritte zu schützen. AppGuard ermöglicht das Durchsetzen von feingranularen Datenschutzrichtlinien für Drittanbieteranwendungen in Android um einen angemessen Schutz der Nutzerdaten zu gewährleisten

Android source code vulnerability detection: a systematic literature review

The use of mobile devices is rising daily in this technological era. A continuous and increasing number of mobile applications are constantly offered on mobile marketplaces to fulfil the needs of smartphone users. Many Android applications do not address the security aspects appropriately. This is often due to a lack of automated mechanisms to identify, test, and fix source code vulnerabilities at the early stages of design and development. Therefore, the need to fix such issues at the initial stages rather than providing updates and patches to the published applications is widely recognized. Researchers have proposed several methods to improve the security of applications by detecting source code vulnerabilities and malicious codes. This Systematic Literature Review (SLR) focuses on Android application analysis and source code vulnerability detection methods and tools by critically evaluating 118 carefully selected technical studies published between 2016 and 2022. It highlights the advantages, disadvantages, applicability of the proposed techniques and potential improvements of those studies. Both Machine Learning (ML) based methods and conventional methods related to vulnerability detection are discussed while focusing more on ML-based methods since many recent studies conducted experiments with ML. Therefore, this paper aims to enable researchers to acquire in-depth knowledge in secure mobile application development while minimizing the vulnerabilities by applying ML methods. Furthermore, researchers can use the discussions and findings of this SLR to identify potential future research and development directions