PrivacyGuard: A VPN-Based Approach to Detect Privacy Leakages on Android Devices

The Internet is now the most important and efficient way to gain information, and mobile devices are the easiest way to access the Internet. Furthermore, wearable devices, which can be considered to be the next generation of mobile devices, are becoming popular. The more people rely on mobile devices, the more private information about these people can be gathered from their devices. If a device is lost or compromised, much private information is revealed. Although today’s smartphone operating systems are trying to provide a secure environment, they still fail to provide users with adequate control over and visibility into how third-party applications use their private data. The privacy leakage problem on mobile devices is still severe. For example, according a field study [1] done by CMU recently, Android applications track users’ location every three minutes in average. After the PRISM program, a surveillance program done by NSA, is exposed, people are becoming increasingly aware of the mobile privacy leakages. However, there are few tools available to average users for privacy preserving. Most tools developed by recent work have some problems (details can be found in chapter 2). To address these problems, we present PrivacyGuard, an efficient way to simultaneously detect leakage of multiple types of sensitive data, such as a phone’s IMEI number or location data. PrivacyGuard provides real-time protection. It is possible to modify the leaked information and replace it with crafted data to achieve protection. PrivacyGuard is configurable, extensible and useful for other research. We implement PrivacyGuard on the Android platform by taking advantage of the VPNService class provided by the Android SDK. PrivacyGuard does not require root per- missions to run on a device and does not require any knowledge about VPN technology from users either. The VPN server runs on the device locally. No external servers are required. According to our experiments, PrivacyGuard can effectively detect privacy leak- ages of most applications and advertisement libraries with almost no overhead on power consumption and reasonable overhead on network speed

Cross-core Microarchitectural Attacks and Countermeasures

In the last decade, multi-threaded systems and resource sharing have brought a number of technologies that facilitate our daily tasks in a way we never imagined. Among others, cloud computing has emerged to offer us powerful computational resources without having to physically acquire and install them, while smartphones have almost acquired the same importance desktop computers had a decade ago. This has only been possible thanks to the ever evolving performance optimization improvements made to modern microarchitectures that efficiently manage concurrent usage of hardware resources. One of the aforementioned optimizations is the usage of shared Last Level Caches (LLCs) to balance different CPU core loads and to maintain coherency between shared memory blocks utilized by different cores. The latter for instance has enabled concurrent execution of several processes in low RAM devices such as smartphones. Although efficient hardware resource sharing has become the de-facto model for several modern technologies, it also poses a major concern with respect to security. Some of the concurrently executed co-resident processes might in fact be malicious and try to take advantage of hardware proximity. New technologies usually claim to be secure by implementing sandboxing techniques and executing processes in isolated software environments, called Virtual Machines (VMs). However, the design of these isolated environments aims at preventing pure software- based attacks and usually does not consider hardware leakages. In fact, the malicious utilization of hardware resources as covert channels might have severe consequences to the privacy of the customers. Our work demonstrates that malicious customers of such technologies can utilize the LLC as the covert channel to obtain sensitive information from a co-resident victim. We show that the LLC is an attractive resource to be targeted by attackers, as it offers high resolution and, unlike previous microarchitectural attacks, does not require core-colocation. Particularly concerning are the cases in which cryptography is compromised, as it is the main component of every security solution. In this sense, the presented work does not only introduce three attack variants that can be applicable in different scenarios, but also demonstrates the ability to recover cryptographic keys (e.g. AES and RSA) and TLS session messages across VMs, bypassing sandboxing techniques. Finally, two countermeasures to prevent microarchitectural attacks in general and LLC attacks in particular from retrieving fine- grain information are presented. Unlike previously proposed countermeasures, ours do not add permanent overheads in the system but can be utilized as preemptive defenses. The first identifies leakages in cryptographic software that can potentially lead to key extraction, and thus, can be utilized by cryptographic code designers to ensure the sanity of their libraries before deployment. The second detects microarchitectural attacks embedded into innocent-looking binaries, preventing them from being posted in official application repositories that usually have the full trust of the customer

Forensic Analysis of Smartphone Applications for Privacy Leakage

Smartphone and tablets are personal devices that have diffused to near universal ubiquity in recent years. As Smartphone users become more privacy-aware and -conscious, research is needed to understand how “leakage” of private information (personally identifiable information – PII) occurs. This study explores how leakage studies in Droid devices should be adapted to Apple iOS devices. The OWASP Zed Attack Proxy (ZAP) is examined for 50 apps in various categories. This study confirms that: (1) most apps transmit unencrypted sensitive PII, (2) SSL is used by some recipient websites, but without corresponding app compliance with SSL, and (3) most apps in iOS environments reveal (leak) smartphone version. The paper concludes that much additional work is needed to assess the privacy dominance between platforms and to raise user awareness of smartphone privacy intrusions. Keywords: mobile forensics, ZAP, privacy leakage, metadata, securit

Detecting Privacy Leaks Through Existing Android Frameworks

The Android application ecosystem has thrived, with hundreds of thousands of applications (apps) available to users; however, not all of them are safe or privacy-friendly. Analyzing these many apps for malicious behaviors is an important but challenging area of research as malicious apps tend to use prevalent stealth techniques, e.g., encryption, code transformation, and other obfuscation approaches to bypass detection. Academic researchers and security companies have realized that the traditional signature-based and static analysis methods are inadequate to deal with this evolving threat. In recent years, a number of static and dynamic code analysis proposals for analyzing Android apps have been introduced in academia and in the commercial world. Moreover, as a single detection approach may be ineffective against advanced obfuscation techniques, multiple frameworks for privacy leakage detection have been shown to yield better results when used in conjunction. In this dissertation, our contribution is two-fold. First, we organize 32 of the most recent and promising privacy-oriented proposals on Android apps analysis into two categories: static and dynamic analysis. For each category, we survey the state of-the-art proposals and provide a high-level overview of the methodology they rely on to detect privacy-sensitive leakages and app behaviors. Second, we choose one popular proposal from each category to analyze and detect leakages in 5,000 Android apps. Our toolchain setup consists of IntelliDroid (static) to find and trigger sensitive API (Application Program Interface) calls in target apps and leverages TaintDroid (dynamic) to detect leakages in these apps. We found that about 33% of the tested apps leak privacy-sensitive information over the network (e.g., IMEI, location, UDID), which is consistent with existing work. Furthermore, we highlight the efficiency of combining IntelliDroid and TaintDroid in comparison with Android Monkey and TaintDroid as used in most prior work. We report an overall increase in the frequency of leakage of identifiers. This increase may indicate that IntelliDroid is a better approach over Android Monkey

Secure Android Code Helper (Sach): A Tool For Assisting Secure Android Application Development

Mobile devices now store a lot of sensitive data. With many users adapting to the technical advancement of mobile devices, security of the user\u27s sensitive data becomes imperative. Security vulnerabilities in the mobile apps will lead to leakage of user’s sensitive data. The goal of this research is to propose a tool to help programmers create secure Android applications. The tool will warn developers about specific classes or methods that include security vulnerabilities such as data leakage and access control vulnerabilities. The tool analyzes Android source code using two approaches: 1) Parse the source code and XML to report vulnerabilities based on CERT secure coding rules for Android application development and 2) Run FlowDroid on source code, parse the output of FlowDroid and look for device ID, GPS location data being leaked to a log file or through implicit intent. The results from these approaches are combined into reports that inform developers of security vulnerabilities. The proof of concept of the tool has been implemented and tested. Future work includes completing implementation of the tool and running tests on a large number of source codes to evaluate its effectiveness

Towards understanding Android system vulnerabilities: Techniques and insights

National Research Foundation (NRF) Singapor