Biblio

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.

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.

Choosing how to write natural language scenarios is challenging, because stakeholders may over-generalize their descriptions or overlook or be unaware of alternate scenarios. In security, for example, this can result in weak security constraints that are too general, or missing constraints. Another challenge is that analysts are unclear on where to stop generating new scenarios. In this paper, we introduce the Multifactor Quality Method (MQM) to help requirements analysts to empirically collect system constraints in scenarios based on elicited expert preferences. The method combines quantitative statistical analysis to measure system quality with qualitative coding to extract new requirements. The method is bootstrapped with minimal analyst expertise in the domain affected by the quality area, and then guides an analyst toward selecting expert-recommended requirements to monotonically increase system quality. We report the results of applying the method to security. This include 550 requirements elicited from 69 security experts during a bootstrapping stage, and subsequent evaluation of these results in a verification stage with 45 security experts to measure the overall improvement of the new requirements. Security experts in our studies have an average of 10 years of experience. Our results show that using our method, we detect an increase in the security quality ratings collected in the verification stage. Finally, we discuss how our proposed method helps to improve security requirements elicitation, analysis, and measurement. 

Requirements analysts can model regulated data practices to identify and reason about risks of noncompliance. If terminology is inconsistent or ambiguous, however, these models and their conclusions will be unreliable. To study this problem, we investigated an approach to automatically construct an information type ontology by identifying information type hyponymy in privacy policies using Tregex patterns. Tregex is a utility to match regular expressions against constituency parse trees, which are hierarchical expressions of natural language clauses, including noun and verb phrases. We discovered the Tregex patterns by applying content analysis to 30 privacy policies from six domains (shopping, telecommunication, social networks, employment, health, and news.) From this dataset, three semantic and four lexical categories of hyponymy emerged based on category completeness and wordorder. Among these, we identified and empirically evaluated 72 Tregex patterns to automate the extraction of hyponyms from privacy policies. The patterns match information type hyponyms with an average precision of 0.72 and recall of 0.74. 

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.

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.

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.

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.

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.

Scientific advancement is fueled by solid fundamental research, followed by replication, meta-analysis, and theory building. To support such advancement, researchers and government agencies have been working towards a "science of security". As in other sciences, security science requires high-quality fundamental research addressing important problems and reporting approaches that capture the information necessary for replication, meta-analysis, and theory building. The goal of this paper is to aid security researchers in establishing a baseline of the state of scientific reporting in security through an analysis of indicators of scientific research as reported in top security conferences, specifically the 2015 ACM CCS and 2016 IEEE S&P proceedings. To conduct this analysis, we employed a series of rubrics to analyze the completeness of information reported in papers relative to the type of evaluation used (e.g. empirical study, proof, discussion). Our findings indicated some important information is often missing from papers, including explicit documentation of research objectives and the threats to validity. Our findings show a relatively small number of replications reported in the literature. We hope that this initial analysis will serve as a baseline against which we can measure the advancement of the science of security.

Traditionally, distributed computing concentrates on computation understood at the level of information exchange and sets aside human and organizational concerns as largely to be handled in an ad hoc manner.  Increasingly, however, distributed applications involve multiple loci of autonomy.  Research in multiagent systems (MAS) addresses autonomy by drawing on concepts and techniques from artificial intelligence.  However, MAS research generally lacks an adequate understanding of modern distributed computing.

In this Blue Sky paper, we envision decentralized multiagent systems as a way to place decentralized intelligence in distributed computing, specifically, by supporting computation at the level of social meanings.  We motivate our proposals for research in the context of the Internet of Things (IoT), which has become a major thrust in distributed computing.  From the IoT's representative applications, we abstract out the major challenges of relevance to decentralized intelligence.  These include the heterogeneity of IoT components; asynchronous and delay-tolerant communication and decoupled enactment; and multiple stakeholders with subtle requirements for governance, incorporating resource usage, cooperation, and privacy.  The IoT yields high-impact problems that require solutions that go beyond traditional ways of thinking.

We conclude with highlights of some possible research directions in decentralized MAS, including programming models; interaction-oriented software engineering; and what we term enlightened governance.

The notion of commitment is widely studied as a high-level abstraction for modeling multiagent interaction.  An important challenge is supporting flexible decentralized enactments of commitment specifications.  In this paper, we combine recent advances on specifying commitments and information protocols.  Specifically, we contribute Tosca, a technique for automatically synthesizing information protocols from commitment specifications. Our main result is that the synthesized protocols support commitment alignment, which is the idea that agents must make compatible inferences about their commitments despite decentralization.

Proactive security reviews and test efforts are a necessary component of the software development lifecycle. Resource limitations often preclude reviewing the entire code
base. Making informed decisions on what code to review can improve a team’s ability to find and remove vulnerabilities. Risk-based attack surface approximation (RASA) is a technique that uses crash dump stack traces to predict what code may contain exploitable vulnerabilities. The goal of this research is to help software development teams prioritize security efforts by the efficient development of a risk-based attack surface approximation. We explore the use of RASA using Mozilla Firefox and Microsoft Windows stack traces from crash dumps. We create RASA at the file level for Firefox, in which the 15.8% of the files that were part of the approximation contained 73.6% of the vulnerabilities seen for the product. We also explore the effect of random sampling of crashes on the approximation, as it may be impractical for organizations to store and process every crash received. We find that 10-fold random sampling of crashes at a rate of 10% resulted in 3% less vulnerabilities identified than using the entire set of stack traces for Mozilla Firefox. Sampling crashes in Windows 8.1 at a rate of 40% resulted in insignificant differences in vulnerability and file coverage as compared to a rate of 100%.

Even though build automation tools help to reduce errors and rapid releases of software changes, use of build automation tools is not widespread amongst software practitioners. Software practitioners perceive build automation tools as complex, which can hinder the adoption of these tools. How well founded such perception is, can be determined by
systematic exploration of adoption factors that influence usage of build automation tools. The goal of this paper is to aid software practitioners in increasing their usage of build
automation tools by identifying the adoption factors that influence usage of these tools. We conducted a survey to empirically identify the adoption factors that influence usage of
build automation tools. We obtained survey responses from 268 software professionals who work at NestedApps, Red Hat, as well as contribute to open source software. We observe that adoption factors related to complexity do not have the strongest influence on usage of build automation tools. Instead, we observe compatibility-related adoption factors, such as adjustment with existing tools, and adjustment with practitioner’s existing workflow, to have influence on usage of build automation tools with greater importance. Findings from our paper suggest that usage of build automation tools might increase if: build automation tools fit well with practitioners’ existing workflow and tool usage; and usage of build automation tools are made more visible among practitioners’ peers.

We seek to address the challenge of engineering socially intelligent personal agents that are privacy-aware. We propose Arnor, a method, including a metamodel based on social constructs. Arnor incorporates social norms and goes beyond existing agent-oriented software engineering (AOSE) methods by systematically capturing how a personal agent’s actions influence the social experience it delivers. We conduct two empirical studies to evaluate Arnor. First, via a multiphase developer study, we show that Arnor simplifies application development. Second, via simulation experiments, we show that Arnor provides improved privacy-preserving social experience to end users than personal agents engineered using a traditional AOSE method.

Services today are configured through policies that capture expected behaviors. However, because of subtle and changing stakeholder requirements, producing and maintaining policies is nontrivial. Policy errors are surprisingly common and cause avoidable security vulnerabilities.

We propose Aragorn, an approach that applies formal argumentation to produce policies that balance stakeholder concerns. We demonstrate empirically that, compared to the traditional approach for specifying policies, Aragorn performs (1) better on coverage, correctness, and quality; (2) equally well on learnability and effort÷coverage and difficulty; and (3) slightly worse on time and effort needed. Thus, Aragorn demonstrates the potential for capturing policy rationales as arguments.

Large software systems have to contend with a significant number of users who interact with different components of the system in various ways. The sequences of components that are used as part of an interaction define sets of behaviors that users have with the system. These can be large in number. Among these users, it is possible that there are some who exhibit anomalous behaviors -- for example, they may have found back doors into the system and are doing something malicious. These anomalous behaviors can be hard to distinguish from normal behavior because of the number of interactions a system may have, or because traces may deviate only slightly from normal behavior. In this paper we describe a model-based approach to cluster sequences of user behaviors within a system and to find suspicious, or anomalous, sequences. We exploit the underlying software architecture of a system to define these sequences. We further show that our approach is better at detecting suspicious activities than other approaches, specifically those that use unigrams and bigrams for anomaly detection. We show this on a simulation of a large scale system based on Amazon Web application style architecture.

We typically only consider running programs that are completely written. Programmers end up inserting ad hoc dummy values into their incomplete programs to receive feedback about dynamic behavior. In this work we suggest an evaluation mechanism for incomplete programs, represented as terms with holes. Rather than immediately failing when a hole is encountered, evaluation propagates holes as far as possible. The result is a substantially tighter development loop. 

Structure editors allow programmers to edit the tree structure of a program directly. This can have cognitive benefits, particularly for novice and end-user programmers. It also simplifies matters for tool designers, because they do not need to contend with malformed program text. This paper introduces Hazelnut, a structure editor based on a small bidirectionally typed lambda calculus extended with holes and a cursor. Hazelnut goes one step beyond syntactic well-formedness: its edit actions operate over statically meaningful incomplete terms. Naïvely, this would force the programmer to construct terms in a rigid “outside-in” manner. To avoid this problem, the action semantics automatically places terms assigned a type that is inconsistent with the expected type inside a hole. This meaningfully defers the type consistency check until the term inside the hole is finished. Hazelnut is not intended as an end-user tool itself. Instead, it serves as a foundational account of typed structure editing. To that end, we describe how Hazelnut’s rich metatheory, which we have mechanized using the Agda proof assistant, serves as a guide when we extend the calculus to include binary sum types. We also discuss various interpretations of holes, and in so doing reveal connections with gradual typing and contextual modal type theory, the Curry-Howard interpretation of contextual modal logic. Finally, we discuss how Hazelnut’s semantics lends itself to implementation as an event-based functional reactive program. Our simple reference implementation is written using js_of_ocaml. 

The C preprocessor is used in many C projects to support variability and portability. However, researchers and practitioners criticize the C preprocessor because of its negative effect on code understanding and maintainability and its error proneness. More importantly, the use of the preprocessor hinders the development of tool support that is standard in other languages, such as automated refactoring. Developers aggravate these problems when using the preprocessor in undisciplined ways (e.g., conditional blocks that do not align with the syntactic structure of the code). In this article, we proposed a catalogue of refactorings and we evaluated the number of application possibilities of the refactorings in practice, the opinion of developers about the usefulness of the refactorings, and whether the refactorings preserve behavior. Overall, we found 5670 application possibilities for the refactorings in 63 real-world C projects. In addition, we performed an online survey among 246 developers, and we submitted 28 patches to convert undisciplined directives into disciplined ones. According to our results, 63% of developers prefer to use the refactored (i.e., disciplined) version of the code instead of the original code with undisciplined preprocessor usage. To verify that the refactorings are indeed behavior preserving, we applied them to more than 36 thousand programs generated automatically using a model of a subset of the C language, running the same test cases in the original and refactored programs. Furthermore, we applied the refactorings to three real-world projects: BusyBox, OpenSSL, and SQLite. This way, we detected and fixed a few behavioral changes, 62% caused by unspecified behavior in the C language.

Enterprise networks today have highly diverse correctness requirements and relatively common performance objectives. As a result, preferred abstractions for enterprise networks are those which allow matching security and correctness specifications, while transparently managing performance. Existing SDN network management architectures, however, bundle correctness and performance as a single abstraction. We argue that this creates an SDN ecosystem that is unnecessarily hard to build, maintain and evolve. We advocate a separation of the diverse correctness abstractions from generic performance optimization, to enable easier evolution of SDN controllers and platforms. We propose Oreo, a first step towards a common and relatively transparent performance optimization layer for SDN. Oreo performs the optimization by first building a model that describes every flow in the network, and then performing network-wide, multi-objective optimization based on this model without disrupting higher level security and correctness. 

A key use of software-defined networking is to enable scale-out of network data plane elements. Naively scaling networking elements, however, can cause incorrect security responses. For example, we show that an IDS system which operates correctly as a single network element can erroneously and permanently block hosts when it is replicated. Similarly, a scaled-out firewall can incorrectly block hosts.

In this paper, we provide a system, COCONUT, for seamless scale-out of network forwarding elements; that is, an SDN application programmer can program to what functionally appears to be a single forwarding element, but which may be replicated behind the scenes. To do this, we identify the key property for seamless scale out, weak causality, and guarantee it through a practical and scalable implementation of vector clocks in the data plane. We formally prove that COCONUT enables seamless scale out of networking elements, i.e., the user-perceived behavior of any COCONUT element implemented with a distributed set of concurrent replicas is provably indistinguishable from its singleton implementation. Finally, we build a prototype of COCONUT and experimentally demonstrate its correct behavior. We also show that its abstraction enables a more efficient implementation of seamless scale-out compared to a naive baseline.

This work was funded by the SoS lablet at the University of Illinois at Urbana-Champaign.

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.

We present security vulnerabilities in the remote voting system Helios. We propose Apollo, a modified version of Helios, which addresses these vulnerabilities and could improve the feasibility of internet voting.

In particular, we note that Apollo does not possess Helios' major known vulnerability, where a dishonest voting terminal can change the vote after it obtains the voter's credential. With Apollo-lite, votes not authorized by the voter are detected by the public and prevented from being included in the tally.

The full version of Apollo enables a voter to prove that her vote was changed. We also describe a very simple protocol for the voter to interact with any devices she employs to check on the voting system, to enable frequent and easy auditing of encryptions and checking of the bulletin board.