Biblio

Modern frameworks are required to be extendable as well as secure. However, these two qualities are often at odds. In this poster we describe an approach that uses a combination of static analysis and run-time management, based on software architecture models, that can improve security while maintaining framework extendability. We implement a prototype of the approach for the Android platform. Static analysis identifies the architecture and communication patterns among the collection of apps on an Android device and which communications might be vulnerable to attack. Run-time mechanisms monitor these potentially vulnerable communication patterns, and adapt the system to either deny them, request explicit approval from the user, or allow them.

Self-adaptive systems overcome many of the limitations of human supervision in complex software-intensive systems by endowing them with the ability to automatically adapt their structure and behavior in the presence of runtime changes. However, adaptation in some classes of systems (e.g., safety-critical) can benefit by receiving information from humans (e.g., acting as sophisticated sensors, decision-makers), or by involving them as system-level effectors to execute adaptations (e.g., when automation is not possible, or as a fallback mechanism). However, human participants are influenced by factors external to the system (e.g., training level, fatigue) that affect the likelihood of success when they perform a task, its duration, or even if they are willing to perform it in the first place. Without careful consideration of these factors, it is unclear how to decide when to involve humans in adaptation, and in which way. In this paper, we investigate how the explicit modeling of human participants can provide a better insight into the trade-offs of involving humans in adaptation. We contribute a formal framework to reason about human involvement in self-adaptation, focusing on the role of human participants as actors (i.e., effectors) during the execution stage of adaptation. The approach consists of: (i) a language to express adaptation models that capture factors affecting human behavior and its interactions with the system, and (ii) a formalization of these adaptation models as stochastic multiplayer games (SMGs) that can be used to analyze human-system-environment interactions. We illustrate our approach in an adaptive industrial middleware used to monitor and manage sensor networks in renewable energy production plants.

The rising popularity of Android and the GUI-driven nature of its apps have motivated the need for applicable automated GUI testing techniques. Although exhaustive testing of all possible combinations is the ideal upper bound in combinatorial testing, it is often infeasible, due to the combinatorial explosion of test cases. This paper presents TrimDroid, a framework for GUI testing of Android apps that uses a novel strategy to generate tests in a combinatorial, yet scalable, fashion. It is backed with automated program analysis and formally rigorous test generation engines. TrimDroid relies on program analysis to extract formal specifications. These speci- fications express the app’s behavior (i.e., control flow between the various app screens) as well as the GUI elements and their dependencies. The dependencies among the GUI elements comprising the app are used to reduce the number of combinations with the help of a solver. Our experiments have corroborated TrimDroid’s ability to achieve a comparable coverage as that possible under exhaustive GUI testing using significantly fewer test cases.

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. 

The use of shared mutable state, commonly seen in object-oriented systems, is often problematic due to the potential conflicting interactions between aliases to the same state. We present a substructural type system outfitted with a novel lightweight interference control mechanism, rely-guarantee protocols, that enables controlled aliasing of shared resources. By assigning each alias separate roles, encoded in a novel protocol abstraction in the spirit of rely-guarantee reasoning, our type system ensures that challenging uses of shared state will never interfere in an unsafe fashion. In particular, rely-guarantee protocols ensure that each alias will never observe an unexpected value, or type, when inspecting shared memory regardless of how the changes to that shared state (originating from potentially unknown program contexts) are interleaved at run-time.

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.

As information security became an increasing
concern for software developers and users, requirements
engineering (RE) researchers brought new insight to security
requirements. Security requirements aim to address security at
the early stages of system design while accommodating the
complex needs of different stakeholders. Meanwhile, other
research communities, such as usable privacy and security,
have also examined these requirements with specialized goal to
make security more usable for stakeholders from product
owners, to system users and administrators. In this paper we
report results from conducting a literature survey to compare
security requirements research from RE Conferences with the
Symposium on Usable Privacy and Security (SOUPS). We
report similarities between the two research areas, such as
common goals, technical definitions, research problems, and
directions. Further, we clarify the differences between these
two communities to understand how they can leverage each
other’s insights. From our analysis, we recommend new
directions in security requirements research mainly to expand
the meaning of security requirements in RE to reflect the
technological advancements that the broader field of security is
experiencing. These recommendations to encourage crosscollaboration
with other communities are not limited to the
security requirements area; in fact, we believe they can be
generalized to other areas of RE.

As information security became an increasing concern for software developers and users, requirements engineering (RE) researchers brought new insight to security requirements. Security requirements aim to address security at the early stages of system design while accommodating the complex needs of different stakeholders. Meanwhile, other research communities, such as usable privacy and security, have also examined these requirements with specialized goal to make security more usable for stakeholders from product owners, to system users and administrators. In this paper we report results from conducting a literature survey to compare security requirements research from RE Conferences with the Symposium on Usable Privacy and Security (SOUPS). We report similarities between the two research areas, such as common goals, technical definitions, research problems, and directions. Further, we clarify the differences between these two communities to understand how they can leverage each other’s insights. From our analysis, we recommend new directions in security requirements research mainly to expand the meaning of security requirements in RE to reflect the technological advancements that the broader field of security is experiencing. These recommendations to encourage crosscollaboration with other communities are not limited to the security requirements area; in fact, we believe they can be generalized to other areas of RE. 

Security requirements around software systems have become more stringent as society becomes more interconnected via the Internet. New ways of prioritizing security efforts are needed so security professionals can use their time effectively to find security vulnerabilities or prevent them from occurring in the first place. The goal of this work is to help software development teams prioritize security efforts by approximating the attack surface of a software system via stack trace analysis. Automated attack surface approximation is a technique that uses crash dump stack traces to predict what code may contain exploitable vulnerabilities. If a code entity (a binary, file or function) appears on stack traces, then Attack Surface Approximation (ASA) considers that code entity is on the attack surface of the software system. We also explore whether number of appearances of code on stack traces correlates with where security vulnerabilities are found. To date, feasibility studies of ASA have been performed on Windows 8 and 8.1, and Mozilla Firefox. The results from these studies indicate that ASA may be useful for practitioners trying to secure their software systems. We are now working towards establishing the ground truth of what the attack surface of software systems is, along with looking at how ASA could change over time, among other metrics.

Privacy remains a major challenge today partly because it brings together social and technical considerations. Yet, current software engineering focuses only on the technical aspects. In contrast, our approach, Revani, understands privacy from the standpoint of sociotechnical systems (STSs), with particular attention on the social elements of STSs. We specify STSs via a combination of technical mechanisms and social norms founded on accountability.

Revani provides a way to formally represent mechanisms and norms, and applies model checking to verify whether specified mechanisms and norms would satisfy the requirements of the stakeholders. Additionally, Revani provides a set of design patterns and a revision tool to update an STS specification as necessary. We demonstrate the working of Revani on a healthcare emergency use case pertaining to disasters.

Risk homeostasis theory claims that individuals adjust their behaviors in response to changing variables to keep what they perceive as a constant accepted level of risk [8]. Risk homeostasis theory is used to explain why drivers may drive faster when wearing seatbelts. Here we explore whether risk homeostasis theory applies to end-user security behaviors. We use observed data from over 200 participants in a longitudinal in-situ study as well as survey data from 249 users to attempt to determine how user security behaviors and attitudes are affected by the presence or absence of antivirus software. If risk compensation is occurring, users might be expected to behave more dangerously in some ways when antivirus is present. Some of our preliminary data suggests that risk compensation may be occurring, but additional work with larger samples is needed. 

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%.

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%.

Social media data and other web-based network data are large and dynamic rendering the identification of structural changes in such systems a hard problem. Typically, online data is constantly streaming and results in data that is incomplete thus necessitating the need to understand the robustness of network metrics on partial or sampled network data. In this paper, we examine the effects of sampling on key network centrality metrics using two empirical communication datasets. Correlations between network metrics of original and sampled nodes offer a measure of sampling accuracy. The relationship between sampling and accuracy is convergent and amenable to nonlinear analysis. Naturally, larger edge samples induce sampled graphs that are more representative of the original graph. However, this effect is attenuated when larger sets of nodes are recovered in the samples. Also, we find that the graph structure plays a prominent role in sampling accuracy. Centralized graphs, in which fewer nodes enjoy higher centrality scores, offer more representative samples.

Process-level races are endemic in modern  systems. These races are difficult  to debug  because they are  sensitive to execution   events  such  as  interrupts and scheduling.  Unless  a process interleaving   that can result in the race can be found, it cannot be reproduced  and cannot be corrected. In practice, however,  the number of interleavings  that can occur among processes  in practice  is large,  and the patterns of interleavings can be complex. Thus, approaches for reproducing process-level races  to date are  often ineffective.  In  this paper, we present RRF, a race reproduction  framework that can help software engineers reproduce reported process-level races, enabling  them to potentially  debug these races. RRF performs a hybrid analysis by leveraging  existing  static program analysis tools, dynamic kernel event  reporting tools,  and yield points  to provide  the observability and controllability  needed to reproduce races. We conducted an empirical study to evaluate RRF; our results show that RRF can be effective for reproducing races.

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.