Ghera: A Repository of Android App Vulnerability Benchmarks

Security of mobile apps affects the security of their users. This has fueled the development of techniques to automatically detect vulnerabilities in mobile apps and help developers secure their apps; specifically, in the context of Android platform due to openness and ubiquitousness of the platform. Despite a slew of research efforts in this space, there is no comprehensive repository of up-to-date and lean benchmarks that contain most of the known Android app vulnerabilities and, consequently, can be used to rigorously evaluate both existing and new vulnerability detection techniques and help developers learn about Android app vulnerabilities. In this paper, we describe Ghera, an open source repository of benchmarks that capture 25 known vulnerabilities in Android apps (as pairs of exploited/benign and exploiting/malicious apps). We also present desirable characteristics of vulnerability benchmarks and repositories that we uncovered while creating Ghera.Comment: 10 pages. Accepted at PROMISE'1

Advanced Security Analysis for Emergent Software Platforms

Emergent software ecosystems, boomed by the advent of smartphones and the Internet of Things (IoT) platforms, are perpetually sophisticated, deployed into highly dynamic environments, and facilitating interactions across heterogeneous domains. Accordingly, assessing the security thereof is a pressing need, yet requires high levels of scalability and reliability to handle the dynamism involved in such volatile ecosystems. This dissertation seeks to enhance conventional security detection methods to cope with the emergent features of contemporary software ecosystems. In particular, it analyzes the security of Android and IoT ecosystems by developing rigorous vulnerability detection methods. A critical aspect of this work is the focus on detecting vulnerable and unsafe interactions between applications that share common components and devices. Contributions of this work include novel insights and methods for: (1) detecting vulnerable interactions between Android applications that leverage dynamic loading features for concealing the interactions; (2) identifying unsafe interactions between smart home applications by considering physical and cyber channels; (3) detecting malicious IoT applications that are developed to target numerous IoT devices; (4) detecting insecure patterns of emergent security APIs that are reused from open-source software. In all of the four research thrusts, we present thorough security analysis and extensive evaluations based on real-world applications. Our results demonstrate that the proposed detection mechanisms can efficiently and effectively detect vulnerabilities in contemporary software platforms. Advisers: Hamid Bagheri and Qiben Ya

A family of droids -- Android malware detection via behavioral modeling: static vs dynamic analysis

Following the increasing popularity of mobile ecosystems, cybercriminals have increasingly targeted them, designing and distributing malicious apps that steal information or cause harm to the device's owner. Aiming to counter them, detection techniques based on either static or dynamic analysis that model Android malware, have been proposed. While the pros and cons of these analysis techniques are known, they are usually compared in the context of their limitations e.g., static analysis is not able to capture runtime behaviors, full code coverage is usually not achieved during dynamic analysis, etc. Whereas, in this paper, we analyze the performance of static and dynamic analysis methods in the detection of Android malware and attempt to compare them in terms of their detection performance, using the same modeling approach. To this end, we build on MaMaDroid, a state-of-the-art detection system that relies on static analysis to create a behavioral model from the sequences of abstracted API calls. Then, aiming to apply the same technique in a dynamic analysis setting, we modify CHIMP, a platform recently proposed to crowdsource human inputs for app testing, in order to extract API calls' sequences from the traces produced while executing the app on a CHIMP virtual device. We call this system AuntieDroid and instantiate it by using both automated (Monkey) and user-generated inputs. We find that combining both static and dynamic analysis yields the best performance, with F-measure reaching 0.92. We also show that static analysis is at least as effective as dynamic analysis, depending on how apps are stimulated during execution, and, finally, investigate the reasons for inconsistent misclassifications across methods.Accepted manuscrip

Eight years of rider measurement in the Android malware ecosystem: evolution and lessons learned

Despite the growing threat posed by Android malware, the research community is still lacking a comprehensive view of common behaviors and trends exposed by malware families active on the platform. Without such view, the researchers incur the risk of developing systems that only detect outdated threats, missing the most recent ones. In this paper, we conduct the largest measurement of Android malware behavior to date, analyzing over 1.2 million malware samples that belong to 1.2K families over a period of eight years (from 2010 to 2017). We aim at understanding how the behavior of Android malware has evolved over time, focusing on repackaging malware. In this type of threats different innocuous apps are piggybacked with a malicious payload (rider), allowing inexpensive malware manufacturing. One of the main challenges posed when studying repackaged malware is slicing the app to split benign components apart from the malicious ones. To address this problem, we use differential analysis to isolate software components that are irrelevant to the campaign and study the behavior of malicious riders alone. Our analysis framework relies on collective repositories and recent advances on the systematization of intelligence extracted from multiple anti-virus vendors. We find that since its infancy in 2010, the Android malware ecosystem has changed significantly, both in the type of malicious activity performed by the malicious samples and in the level of obfuscation used by malware to avoid detection. We then show that our framework can aid analysts who attempt to study unknown malware families. Finally, we discuss what our findings mean for Android malware detection research, highlighting areas that need further attention by the research community.Accepted manuscrip

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

STATIC AND DYNAMIC ANALYSES FOR PROTECTING THE JAVA SOFTWARE EXECUTION ENVIRONMENT

In my thesis, I present three projects on which I have worked during my Ph.D. studies. All of them focus on software protection in the Java environment with static and dynamic techniques for control-flow and data-dependency analysis. More specifically, the first two works are dedicated to the problem of deserialization of untrusted data in Java. In the first, I present a defense system that was designed for protecting the Java Virtual Machine, along with the results that were obtained. In the second, I present a recent research project that aims at automatic generation of deserialization attacks, to help identifying them and increasing protection. The last discussed work concerns another branch of software protection: the authentication on short-distance channels (or the lack thereof) in Android APKs. In said work, I present a tool that was built for automatically identifying the presence of high-level authentication in Android apps. I thoroughly discuss experiments, limitations and future work for all three projects, concluding with general principles that bring these works together, and can be applied when facing related security issues in high-level software protection

The Evolution of Android Malware and Android Analysis Techniques

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