Biblio

Modern mobile platforms rely on a permission model to guard the system's resources and apps. In Android, since the permissions are granted at the granularity of apps, and all components belonging to an app inherit those permissions, an app's components are typically over-privileged, i.e., components are granted more privileges than they need to complete their tasks. Systematic violation of least-privilege principle in Android has shown to be the root cause of many security vulnerabilities. To mitigate this issue, we have developed DELDROID, an automated system for determination of least privilege architecture in Android and its enforcement at runtime. A key contribution of our approach is the ability to limit the privileges granted to apps without the need to modify them. DELDROID utilizes static program analysis techniques to extract the exact privileges each component needs for providing its functionality. A Multiple-Domain Matrix representation of the system's architecture is then used to automatically analyze the security posture of the system and derive its least-privilege architecture. Our experiments on hundreds of real world apps corroborate DELDROID's ability in effectively establishing the least-privilege architecture and its benefits in alleviating the security threats.

Information security can benefit from real-time cyber threat indicator sharing, in which companies and government agencies share their knowledge of emerging cyberattacks to benefit their sector and society at large. As attacks become increasingly sophisticated by exploiting behavioral dimensions of human computer operators, there is an increased risk to systems that store personal information. In addition, risk increases as individuals blur the boundaries between workplace and home computing (e.g., using workplace computers for personal reasons). This paper describes an architecture to leverage individual perceptions of privacy risk to compute privacy risk scores over cyber threat indicator data. Unlike security risk, which is a risk to a particular system, privacy risk concerns an individual's personal information being accessed and exploited. The architecture integrates tools to extract information entities from textual threat reports expressed in the STIX format and privacy risk estimates computed using factorial vignettes to survey individual risk perceptions. The architecture aims to optimize for scalability and adaptability to achieve real-time risk scoring.

Race conditions are difficult to detect because they usually occur only under specific execution interleavings. Numerous program analysis and testing techniques have been proposed to detect race conditions between threads on single applications. However, most of these techniques neglect races that occur at the process level due to complex system event interactions. This article presents a framework, SIMEXPLORER, that allows engineers to effectively test for process-level race conditions. SIMEXPLORER first uses dynamic analysis techniques to observe system execution, identify program locations of interest, and report faults related to oracles. Next, it uses virtualization to achieve the fine-grained controllability needed to exercise event interleavings that are likely to expose races. We evaluated the effectiveness of SIMEXPLORER on 24 real-world applications containing both known and unknown process-level race conditions. Our results show that SIMEXPLORER is effective at detecting these race conditions, while incurring an overhead that is acceptable given its effectiveness improvements.

Many authentication schemes ask users to manually compare compact representations of cryptographic keys, known as fingerprints. If the fingerprints do not match, that may signal a man-in-the-middle attack. An adversary performing an attack may use a fingerprint that is similar to the target fingerprint, but not an exact match, to try to fool inattentive users. Fingerprint representations should thus be both usable and secure. We tested the usability and security of eight fingerprint representations under different configurations. In a 661-participant between-subjects experiment, participants compared fingerprints under realistic conditions and were subjected to a simulated attack. The best configuration allowed attacks to succeed 6% of the time; the worst 72%. We find the seemingly effective compare-and-select approach performs poorly for key fingerprints and that graphical fingerprint representations, while intuitive and fast, vary in performance. We identify some fingerprint representations as particularly promising.

When multiple apps on an Android platform interact, faults and security vulnerabilities can occur. Software engineers need to be able to analyze interacting apps to detect such problems. Current approaches for performing such analyses, however, do not scale to the numbers of apps that may need to be considered, and thus, are impractical for application to realworld scenarios. In this paper, we introduce JITANA, a program analysis framework designed to analyze multiple Android apps simultaneously. By using a classloader-based approach instead of a compiler-based approach such as SOOT, JITANA is able to simultaneously analyze large numbers of interacting apps, perform on-demand analysis of large libraries, and effectively analyze dynamically generated code. Empirical studies of JITANA show that it is substantially more efficient than a state-of-theart approach, and that it can effectively and efficiently analyze complex apps including Facebook, Pokemon Go, and Pandora ´ that the state-of-the-art approach cannot handle.

Non-volatile memory express (NVMe) based SSDs and the NUMA platform are widely adopted in servers to achieve faster storage speed and more powerful processing capability. As of now, very little research has been conducted to investigate the performance and energy efficiency of the stateof-the-art NUMA architecture integrated with NVMe SSDs, an emerging technology used to host parallel I/O threads. As this technology continues to be widely developed and adopted, we need to understand the runtime behaviors of such systems in order to design software runtime systems that deliver optimal performance while consuming only the necessary amount of energy. This paper characterizes the runtime behaviors of a Linuxbased NUMA system employing multiple NVMe SSDs. Our comprehensive performance and energy-efficiency study using massive numbers of parallel I/O threads shows that the penalty due to CPU contention is much smaller than that due to remote access of NVMe SSDs. Based on this insight, we develop a dynamic “lesser evil” algorithm called ESN, to minimize the impact of these two types of penalties. ESN is an energyefficient profiling-based I/O thread scheduler for managing I/O threads accessing NVMe SSDs on NUMA systems. Our empirical evaluation shows that ESN can achieve optimal I/O throughput and latency while consuming up to 50% less energy and using fewer CPUs.

Though immutability has been long-proposed as a way to prevent bugs in software, little is known about how to make immutability support in programming languages effective for software engineers. We designed a new formalism that extends Java to support transitive class immutability, the form of immutability for which there is the strongest empirical support, and implemented that formalism in a tool called Glacier. We applied Glacier successfully to two real-world systems. We also compared Glacier to Java’s final in a user study of twenty participants. We found that even after being given instructions on how to express immutability with final, participants who used final were unable to express immutability correctly, whereas almost all participants who used Glacier succeeded. We also asked participants to make specific changes to immutable classes and found that participants who used final all incorrectly mutated immutable state, whereas almost all of the participants who used Glacier succeeded. Glacier represents a promising approach to enforcing immutability in Java and provides a model for enforcement in other languages.

Certification schemes exist to regulate software systems and prevent them from being deployed before they are judged fit to use. However, practitioners are often unsatisfied with the efficiency of certification standards and processes. In this study, we analyzed two certification standards, Common Criteria and DO-178C, and collected insights from literature and from interviews with subject-matter experts to identify concepts affecting the efficiency of certification processes. Our results show that evaluation time, reusability of evaluation artifacts, and composition of systems and certified artifacts are barriers to achieve efficient certification.

Programming language definitions assign formal meaning to complete programs. Programmers, however, spend a substantial amount of time interacting with incomplete programs -- programs with holes, type inconsistencies and binding inconsistencies -- using tools like program editors and live programming environments (which interleave editing and evaluation). Semanticists have done comparatively little to formally characterize (1) the static and dynamic semantics of incomplete programs; (2) the actions available to programmers as they edit and inspect incomplete programs; and (3) the behavior of editor services that suggest likely edit actions to the programmer based on semantic information extracted from the incomplete program being edited, and from programs that the system has encountered in the past. As such, each tool designer has largely been left to develop their own ad hoc heuristics. 
This paper serves as a vision statement for a research program that seeks to develop these "missing" semantic foundations. Our hope is that these contributions, which will take the form of a series of simple formal calculi equipped with a tractable metatheory, will guide the design of a variety of current and future interactive programming tools, much as various lambda calculi have guided modern language designs. Our own research will apply these principles in the design of Hazel, an experimental live lab notebook programming environment designed for data science tasks. We plan to co-design the Hazel language with the editor so that we can explore concepts such as edit-time semantic conflict resolution mechanisms and mechanisms that allow library providers to install library-specific editor services.

The principle of least authority states that each component of the system should be given authority to access only the information and resources that it needs for its operation. This principle is fundamental to the secure design of software systems, as it helps to limit an application’s attack surface and to isolate vulnerabilities and faults. Unfortunately, current programming languages do not provide adequate help in controlling the authority of application modules, an issue that is particularly acute in the case of untrusted third-party extensions. In this paper, we present a language design that facilitates controlling the authority granted to each application module. The key technical novelty of our approach is that modules are firstclass, statically typed capabilities. First-class modules are essentially objects, and so we formalize our module system by translation into an object calculus and prove that the core calculus is typesafe and authority-safe. Unlike prior formalizations, our work defines authority non-transitively, allowing engineers to reason about software designs that use wrappers to provide an attenuated version of a more powerful capability. Our approach allows developers to determine a module’s authority by examining the capabilities passed as module arguments when the module is created, or delegated to the module later during execution. The type system facilitates this by identifying which objects provide capabilities to sensitive resources, and by enabling security architects to examine the capabilities passed into and out of a module based only on the module’s interface, without needing to examine the module’s implementation code. An implementation of the module system and illustrative examples in the Wyvern programming language suggest that our approach can be a practical way to control module authority.

Dynamic adaptation should not leave a software system in an inconsistent state, as it could lead to failure. Prior research has used inter-component dependency models of a system to determine a safe interval for the adaptation of its components, where the most important tradeoff is between disruption in the operations of the system and reachability of safe intervals. This article presents Savasana, which automatically analyzes a software system’s code to extract both inter- and intra-component dependencies. In this way, Savasana is able to obtain more fine-grained models compared to previous approaches. Savasana then uses the detailed models to find safe adaptation intervals that cannot be determined using techniques from prior research. This allows Savasana to achieve a better tradeoff between disruption and reachability. The article demonstrates how Savasana infers safe adaptation intervals for components of a software system under various use cases and conditions.

In parallel with the meteoric rise of mobile software, we are witnessing an alarming escalation in the number and sophistication of the security threats targeted at mobile platforms, particularly Android, as the dominant platform. While existing research has made significant progress towards detection and mitigation of Android security, gaps and challenges remain. This paper contributes a comprehensive taxonomy to classify and characterize the state-of-the-art research in this area. We have carefully followed the systematic literature review process, and analyzed the results of more than 300 research papers, resulting in the most comprehensive and elaborate investigation of the literature in this area of research. The systematic analysis of the research literature has revealed patterns, trends, and gaps in the existing literature, and underlined key challenges and opportunities that will shape the focus of future research efforts.

The Security Behavior Observatory (SBO) is a longitudinal field-study of computer security habits that provides a novel dataset for validating computer security metrics. This paper demonstrates a new strategy for validating phishing detection ability metrics by comparing performance on a phishing signal detection task with data logs found in the SBO. We report: (1) a test of the robustness of performance on the signal detection task by replicating Canfield, Fischhoff and Davis (2016), (2) an assessment of the task's construct validity, and (3) evaluation of its predictive validity using data logs. We find that members of the SBO sample had similar signal detection ability compared to members of the previous mTurk sample and that performance on the task correlated with the Security Behavior Intentions Scale (SeBIS). However, there was no evidence of predictive validity, as the signal detection task performance was unrelated to computer security outcomes in the SBO, including the presence of malicious URLs, malware, and malicious files. We discuss the implications of these findings and the challenges of comparing behavior on structured experimental tasks to behavior in complex real-world settings.