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

Detecting and characterizing self hiding behavior in android applications

Applications (apps) that conceal their activities are fundamentally deceptive; app marketplaces and end-users should treat such apps as suspicious. However, due to its nature and intent, activity concealing is not disclosed up-front, which puts users at risk. This study focuses on characterization and detection of such techniques, e.g., hiding the app or removing traces, known as \u27self hiding\u27 (SH) behavior. SH behavior has not been studied per se - rather it has been reported on only as a byproduct of malware investigations. This gap is addressed via a study and suite of static analyses targeted at SH in Android apps. SH behavior ranges from hiding the app\u27s presence or activity to covering an app\u27s traces, e.g., by blocking phone calls/ text messages or removing calls and messages from logs. Using static analysis tools on a large dataset of 9,452 Android apps (benign as well as malicious) the frequency of 12 such SH behaviors is exposed. It has revealed that malicious apps employ 1.5 SH behaviors per app on average. Surprisingly, SH behavior is also employed by legitimate (\u27benign\u27) apps, which can affect users negatively in multiple ways. The approach has high precision and recall (combined F-measure = 87.19%). This approach is also efficient, with analysis typically taking just 37 seconds per app

Do Androids Dream of Electric Sheep? On Privacy in the Android Supply Chain

The Android Open Source Project (AOSP) was first released by Google in 2008 and has since become the most used operating system [Andaf]. Thanks to the openness of its source code, any smartphone vendor or original equipment manufacturer (OEM) can modify and adapt Android to their specific needs, or add proprietary features before installing it on their devices in order to add custom features to differentiate themselves from competitors. This has created a complex and diverse supply chain, completely opaque to end-users, formed by manufacturers, resellers, chipset manufacturers, network operators, and prominent actors of the online industry that partnered with OEMs. Each of these stakeholders can pre-install extra apps, or implement proprietary features at the framework level. However, such customizations can create privacy and security threats to end-users. Preinstalled apps are privileged by the operating system, and can therefore access system APIs or personal data more easily than apps installed by the user. Unfortunately, despite these potential threats, there is currently no end-to-end control over what apps come pre-installed on a device and why, and no traceability of the different software and hardware components used in a given Android device. In fact, the landscape of pre-installed software in Android and its security and privacy implications has largely remained unexplored by researchers. In this thesis, I investigate the customization of Android devices and their impact on the privacy and security of end-users. Specifically, I perform the first large-scale and systematic analysis of pre-installed Android apps and the supply chain. To do so, I first develop an app, Firmware Scanner [Sca], to crowdsource close to 34,000 Android firmware versions from 1,000 different OEMs from all over the world. This dataset allows us to map the stakeholders involved in the supply chain and their relationships, from device manufacturers and mobile network operators to third-party organizations like advertising and tracking services, and social network platforms. I could identify multiple cases of privacy-invasive and potentially harmful behaviors. My results show a disturbing lack of transparency and control over the Android supply chain, thus showing that it can be damageable privacy- and security-wise to end-users. Next, I study the evolution of the Android permission system, an essential security feature of the Android framework. Coupled with other protection mechanisms such as process sandboxing, the permission system empowers users to control what sensitive resources (e.g., user contacts, the camera, location sensors) are accessible to which apps. The research community has extensively studied the permission system, but most previous studies focus on its limitations or specific attacks. In this thesis, I present an up-to-date view and longitudinal analysis of the evolution of the permissions system. I study how some lesser-known features of the permission system, specifically permission flags, can impact the permission granting process, making it either more restrictive or less. I then highlight how pre-installed apps developers use said flags in the wild and focus on the privacy and security implications. Specifically, I show the presence of third-party apps, installed as privileged system apps, potentially using said features to share resources with other third-party apps. Another salient feature of the permission system is its extensibility: apps can define their own custom permissions to expose features and data to other apps. However, little is known about how widespread the usage of custom permissions is, and what impact these permissions may have on users’ privacy and security. In the last part of this thesis, I investigate the exposure and request of custom permissions in the Android ecosystem and their potential for opening privacy and security risks. I gather a 2.2-million-app-large dataset of both pre-installed and publicly available apps using both Firmware Scanner and purpose-built app store crawlers. I find the usage of custom permissions to be pervasive, regardless of the origin of the apps, and seemingly growing over time. Despite this prevalence, I find that custom permissions are virtually invisible to end-users, and their purpose is mostly undocumented. While Google recommends that developers use their reverse domain name as the prefix of their custom permissions [Gpla], I find widespread violations of this recommendation, making sound attribution at scale virtually impossible. Through static analysis methods, I demonstrate that custom permissions can facilitate access to permission-protected system resources to apps that lack those permissions, without user awareness. Due to the lack of tools for studying such risks, I design and implement two tools, PermissionTracer [Pere] and PermissionTainter [Perd] to study custom permissions. I highlight multiple cases of concerning use of custom permissions by Android apps in the wild. In this thesis, I systematically studied, at scale, the vast and overlooked ecosystem of preinstalled Android apps. My results show a complete lack of control of the supply chain which is worrying, given the huge potential impact of pre-installed apps on the privacy and security of end-users. I conclude with a number of open research questions and future avenues for further research in the ecosystem of the supply chain of Android devices.This work has been supported by IMDEA Networks InstitutePrograma de Doctorado en Ingeniería Telemática por la Universidad Carlos III de MadridPresidente: Douglas Leith.- Secretario: Rubén Cuevas Rumín.- Vocal: Hamed Haddad

Dynamic monitoring of Android malware behavior: a DNS-based approach

The increasing technological revolution of the mobile smart devices fosters their wide use. Since mobile users rely on unofficial or thirdparty repositories in order to freely install paid applications, lots of security and privacy issues are generated. Thus, at the same time that Android phones become very popular and growing rapidly their market share, so it is the number of malicious applications targeting them. Yet, current mobile malware detection and analysis technologies are very limited and ineffective. Due to the particular traits of mobile devices such as the power consumption constraints that make unaffordable to run traditional PC detection engines on the device; therefore mobile security faces new challenges, especially on dynamic runtime malware detection. This approach is import because many instructions or infections could happen after an application is installed or executed. On the one hand, recent studies have shown that the network-based analysis, where applications could be also analyzed by observing the network traffic they generate, enabling us to detect malicious activities occurring on the smart device. On the other hand, the aggressors rely on DNS to provide adjustable and resilient communication between compromised client machines and malicious infrastructure. So, having rich DNS traffic information is very important to identify malevolent behavior, then using DNS for malware detection is a logical step in the dynamic analysis because malicious URLs are common and the present danger for cybersecurity. Therefore, the main goal of this thesis is to combine and correlate two approaches: top-down detection by identifying malware domains using DNS traces at the network level, and bottom-up detection at the device level using the dynamic analysis in order to capture the URLs requested on a number of applications to pinpoint the malware. For malware detection and visualization, we propose a system which is based on dynamic analysis of API calls. Thiscan help Android malware analysts in visually inspecting what the application under study does, easily identifying such malicious functions. Moreover, we have also developed a framework that automates the dynamic DNS analysis of Android malware where the captured URLs at the smartphone under scrutiny are sent to a remote server where they are: collected, identified within the DNS server records, mapped the extracted DNS records into this server in order to classify them either as benign or malicious domain. The classification is done through the usage of machine learning. Besides, the malicious URLs found are used in order to track and pinpoint other infected smart devices, not currently under monitoring

ACMiner: Extraction and Analysis of Authorization Checks in Android's Middleware

Billions of users rely on the security of the Android platform to protect phones, tablets, and many different types of consumer electronics. While Android's permission model is well studied, the enforcement of the protection policy has received relatively little attention. Much of this enforcement is spread across system services, taking the form of hard-coded checks within their implementations. In this paper, we propose Authorization Check Miner (ACMiner), a framework for evaluating the correctness of Android's access control enforcement through consistency analysis of authorization checks. ACMiner combines program and text analysis techniques to generate a rich set of authorization checks, mines the corresponding protection policy for each service entry point, and uses association rule mining at a service granularity to identify inconsistencies that may correspond to vulnerabilities. We used ACMiner to study the AOSP version of Android 7.1.1 to identify 28 vulnerabilities relating to missing authorization checks. In doing so, we demonstrate ACMiner's ability to help domain experts process thousands of authorization checks scattered across millions of lines of code