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Davis, Agnes, Shashidharan, Ashwin, Liu, Qian, Enck, William, McLaughlin, Anne, Watson, Benjamin.  2014.  Insecure Behaviors on Mobile Devices Under Stress. Proceedings of the 2014 Symposium and Bootcamp on the Science of Security. :31:1–31:2.

One of the biggest challenges in mobile security is human behavior. The most secure password may be useless if it is sent as a text or in an email. The most secure network is only as secure as its most careless user. Thus, in the current project we sought to discover the conditions under which users of mobile devices were most likely to make security errors. This scaffolds a larger project where we will develop automatic ways of detecting such environments and eventually supporting users during these times to encourage safe mobile behaviors.

Deshotels, Luke, Deaconescu, Razvan, Carabas, Costin, Manda, Iulia, Enck, William, Chiroiu, Mihai, Li, Ninghui, Sadeghi, Ahmad-Reza.  2018.  iOracle: Automated Evaluation of Access Control Policies in iOS. Proceedings of the 2018 on Asia Conference on Computer and Communications Security. :117-131.

Modern operating systems, such as iOS, use multiple access control policies to define an overall protection system. However, the complexity of these policies and their interactions can hide policy flaws that compromise the security of the protection system. We propose iOracle, a framework that logically models the iOS protection system such that queries can be made to automatically detect policy flaws. iOracle models policies and runtime context extracted from iOS firmware images, developer resources, and jailbroken devices, and iOracle significantly reduces the complexity of queries by modeling policy semantics. We evaluate iOracle by using it to successfully triage executables likely to have policy flaws and comparing our results to the executables exploited in four recent jailbreaks. When applied to iOS 10, iOracle identifies previously unknown policy flaws that allow attackers to modify or bypass access control policies. For compromised system processes, consequences of these policy flaws include sandbox escapes (with respect to read/write file access) and changing the ownership of arbitrary files. By automating the evaluation of iOS access control policies, iOracle provides a practical approach to hardening iOS security by identifying policy flaws before they are exploited.

Deshotels, Luke, Deaconescu, Razvan, Chiroiu, Mihai, Davi, Lucas, Enck, William, Sadeghi, Ahmad-Reza.  2016.  SandScout: Automatic Detection of Flaws in iOS Sandbox Profiles. Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security. :704–716.

Recent literature on iOS security has focused on the malicious potential of third-party applications, demonstrating how developers can bypass application vetting and code-level protections. In addition to these protections, iOS uses a generic sandbox profile called "container" to confine malicious or exploited third-party applications. In this paper, we present the first systematic analysis of the iOS container sandbox profile. We propose the SandScout framework to extract, decompile, formally model, and analyze iOS sandbox profiles as logic-based programs. We use our Prolog-based queries to evaluate file-based security properties of the container sandbox profile for iOS 9.0.2 and discover seven classes of exploitable vulnerabilities. These attacks affect non-jailbroken devices running later versions of iOS. We are working with Apple to resolve these attacks, and we expect that SandScout will play a significant role in the development of sandbox profiles for future versions of iOS.

Liu, Qian, Bae, Juhee, Watson, Benjamin, McLaughhlin, Anne, Enck, William.  2014.  Modeling and Sensing Risky User Behavior on Mobile Devices. Proceedings of the 2014 Symposium and Bootcamp on the Science of Security. :33:1–33:2.

As mobile technology begins to dominate computing, understanding how their use impacts security becomes increasingly important. Fortunately, this challenge is also an opportunity: the rich set of sensors with which most mobile devices are equipped provide a rich contextual dataset, one that should enable mobile user behavior to be modeled well enough to predict when users are likely to act insecurely, and provide cognitively grounded explanations of those behaviors. We will evaluate this hypothesis with a series of experiments designed first to confirm that mobile sensor data can reliably predict user stress, and that users experiencing such stress are more likely to act insecurely.

Shu, Rui, Gu, Xiaohui, Enck, William.  2017.  A Study of Security Vulnerabilities on Docker Hub. Proceedings of the Seventh ACM on Conference on Data and Application Security and Privacy. :269–280.
Docker containers have recently become a popular approach to provision multiple applications over shared physical hosts in a more lightweight fashion than traditional virtual machines. This popularity has led to the creation of the Docker Hub registry, which distributes a large number of official and community images. In this paper, we study the state of security vulnerabilities in Docker Hub images. We create a scalable Docker image vulnerability analysis (DIVA) framework that automatically discovers, downloads, and analyzes both official and community images on Docker Hub. Using our framework, we have studied 356,218 images and made the following findings: (1) both official and community images contain more than 180 vulnerabilities on average when considering all versions; (2) many images have not been updated for hundreds of days; and (3) vulnerabilities commonly propagate from parent images to child images. These findings demonstrate a strong need for more automated and systematic methods of applying security updates to Docker images and our current Docker image analysis framework provides a good foundation for such automatic security update. This article is summarized in: the morning paper an interesting/influential/important paper from the world of CS every weekday morning, as selected by Adrian Colyer
Shu, Rui, Wang, Peipei, Gorski III, Sigmund A, Andow, Benjamin, Nadkarni, Adwait, Deshotels, Luke, Gionta, Jason, Enck, William, Gu, Xiaohui.  2016.  A Study of Security Isolation Techniques. ACM Comput. Surv.. 49:50:1–50:37.

Security isolation is a foundation of computing systems that enables resilience to different forms of attacks. This article seeks to understand existing security isolation techniques by systematically classifying different approaches and analyzing their properties. We provide a hierarchical classification structure for grouping different security isolation techniques. At the top level, we consider two principal aspects: mechanism and policy. Each aspect is broken down into salient dimensions that describe key properties. We break the mechanism into two dimensions, enforcement location and isolation granularity, and break the policy aspect down into three dimensions: policy generation, policy configurability, and policy lifetime. We apply our classification to a set of representative articles that cover a breadth of security isolation techniques and discuss tradeoffs among different design choices and limitations of existing approaches.

Wang, Ruowen, Azab, Ahmed M., Enck, William, Li, Ninghui, Ning, Peng, Chen, Xun, Shen, Wenbo, Cheng, Yueqiang.  2017.  SPOKE: Scalable Knowledge Collection and Attack Surface Analysis of Access Control Policy for Security Enhanced Android. Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security. :612–624.

SEAndroid is a mandatory access control (MAC) framework that can confine faulty applications on Android. Nevertheless, the effectiveness of SEAndroid enforcement depends on the employed policy. The growing complexity of Android makes it difficult for policy engineers to have complete domain knowledge on every system functionality. As a result, policy engineers sometimes craft over-permissive and ineffective policy rules, which unfortunately increased the attack surface of the Android system and have allowed multiple real-world privilege escalation attacks. We propose SPOKE, an SEAndroid Policy Knowledge Engine, that systematically extracts domain knowledge from rich-semantic functional tests and further uses the knowledge for characterizing the attack surface of SEAndroid policy rules. Our attack surface analysis is achieved by two steps: 1) It reveals policy rules that cannot be justified by the collected domain knowledge. 2) It identifies potentially over-permissive access patterns allowed by those unjustified rules as the attack surface. We evaluate SPOKE using 665 functional tests targeting 28 different categories of functionalities developed by Samsung Android Team. SPOKE successfully collected 12,491 access patterns for the 28 categories as domain knowledge, and used the knowledge to reveal 320 unjustified policy rules and 210 over-permissive access patterns defined by those rules, including one related to the notorious libstagefright vulnerability. These findings have been confirmed by policy engineers.

Xie, Tao, Enck, William.  2016.  Text Analytics for Security: Tutorial. Proceedings of the Symposium and Bootcamp on the Science of Security. :124–125.

Computing systems that make security decisions often fail to take into account human expectations. This failure occurs because human expectations are typically drawn from in textual sources (e.g., mobile application description and requirements documents) and are hard to extract and codify. Recently, researchers in security and software engineering have begun using text analytics to create initial models of human expectation. In this tutorial, we provide an introduction to popular techniques and tools of natural language processing (NLP) and text mining, and share our experiences in applying text analytics to security problems. We also highlight the current challenges of applying these techniques and tools for addressing security problems. We conclude the tutorial with discussion of future research directions.

Yang, Wei, Xiao, Xusheng, Pandita, Rahul, Enck, William, Xie, Tao.  2014.  Improving Mobile Application Security via Bridging User Expectations and Application Behaviors. Proceedings of the 2014 Symposium and Bootcamp on the Science of Security. :32:1–32:2.

To keep malware out of mobile application markets, existing techniques analyze the security aspects of application behaviors and summarize patterns of these security aspects to determine what applications do. However, user expectations (reflected via user perception in combination with user judgment) are often not incorporated into such analysis to determine whether application behaviors are within user expectations. This poster presents our recent work on bridging the semantic gap between user perceptions of the application behaviors and the actual application behaviors.