Biblio

In this paper we explore the differential perceptions of cybersecurity professionals and general users regarding access rules and passwords. We conducted a preliminary survey involving 28 participants: 15 cybersecurity professionasl and 13 general users. We present our preliminary findings and explain how such survey data might be used to improve security in practice. We focus on user fatigue with access rules and passwords.

Computer security problems often occur when there are disconnects between users’ understanding of their role in computer security and what is expected of them. To help users make good security decisions more easily, we need insights into the challenges they face in their daily computer usage. We built and deployed the Security Behavior Observatory (SBO) to collect data on user behavior and machine configurations from participants’ home computers. Combining SBO data with user interviews, this paper presents a qualitative study comparing users’ attitudes, behaviors, and understanding of computer security to the actual states of their computers. Qualitative inductive thematic analysis of the interviews produced “engagement” as the overarching theme, whereby participants with greater engagement in computer security and maintenance did not necessarily have more secure computer states. Thus, user engagement alone may not be predictive of computer security. We identify several other themes that inform future directions for better design and research into security interventions. Our findings emphasize the need for better understanding of how users’ computers get infected, so that we can more effectively design user-centered mitigations.

Modern Internet applications are being disaggregated into a microservice-based architecture, with services being updated and deployed hundreds of times a day. The accelerated software life cycle and heterogeneity of language runtimes in a single application necessitates a new approach for testing the resiliency of these applications in production infrastructures. We present Gremlin, a framework for systematically testing the failure-handling capabilities of microservices.  Gremlin is based on the observation that microservices are loosely coupled and thus rely on standard message-exchange patterns over the network. Gremlin allows the operator to easily design tests and executes them by manipulating inter-service messages at the network layer. We show how to use Gremlin to express common failure scenarios and how developers of an enterprise application were able to discover previously unknown bugs in their failure-handling code without modifying the application.

Anonymous messaging platforms like Whisper and Yik Yak allow users to spread messages over a network (e.g., a social network) without revealing message authorship to other users. The spread of messages on these platforms can be modeled by a diffusion process over a graph. Recent advances in network analysis have revealed that such diffusion processes are vulnerable to author deanonymization by adversaries with access to metadata, such as timing information. In this work, we ask the fundamental question of how to propagate anonymous messages over a graph to make it difficult for adversaries to infer the source. In particular, we study the performance of a message propagation protocol called adaptive diffusion introduced in (Fanti et al., 2015). We prove that when the adversary has access to metadata at a fraction of corrupted graph nodes, adaptive diffusion achieves asymptotically optimal source-hiding and significantly outperforms standard diffusion. We further demonstrate empirically that adaptive diffusion hides the source effectively on real social networks.
 

As the dominant mobile computing platform, Android has become a prime target for cyber-security attacks. Many of these attacks are manifested at the application level, and through the exploitation of vulnerabilities in apps downloaded from the popular app stores. Increasingly, sophisticated attacks exploit the vulnerabilities in multiple installed apps, making it extremely difficult to foresee such attacks, as neither the app developers nor the store operators know a priori which apps will be installed together. This paper presents an approach that allows the end-users to safeguard a given bundle of apps installed on their device from such attacks. The approach, realized in a tool, called SEPAR, combines static analysis with lightweight formal methods to automatically infer security-relevant properties from a bundle of apps. It then uses a constraint solver to synthesize possible security exploits, from which fine-grained security policies are derived and automatically enforced to protect a given device. In our experiments with over 4,000 Android apps, SEPAR has proven to be highly effective at detecting previously unknown vulnerabilities as well as preventing their exploitation.

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. 

Anonymous messaging applications have recently gained popularity as a means for sharing opinions without fear of judgment or repercussion. These messages propagate anonymously over a network, typically de ned by social connections or physical proximity. However, recent advances in rumor source detection show that the source of such an anonymous message can be inferred by certain statistical inference attacks. Adaptive di usion was recently proposed as a solution that achieves optimal source obfuscation over regular trees. However, in real social networks, the degrees difer from node to node, and adaptive di usion can be signicantly sub-optimal. This gap increases as the degrees become more irregular.

In order to quantify this gap, we model the underlying network as coming from standard branching processes with i.i.d. degree distributions. Building upon the analysis techniques from branching processes, we give an analytical characterization of the dependence of the probability of detection achieved by adaptive di usion on the degree distribution. Further, this analysis provides a key insight: passing a rumor to a friend who has many friends makes the source more ambiguous. This leads to a new family of protocols that we call Preferential Attachment Adaptive Di usion (PAAD). When messages are propagated according to PAAD, we give both the MAP estimator for nding the source and also an analysis of the probability of detection achieved by this adversary. The analytical results are not directly comparable, since the adversary's observed information has a di erent distribution under adaptive di usion than under PAAD. Instead, we present results from numerical experiments that suggest that PAAD achieves a lower probability of detection, at the cost of increased communication for coordination.

Firewall policies are notorious for having misconfiguration errors which can defeat its intended purpose of protecting hosts in the network from malicious users. We believe this is because today's firewall policies are mostly monolithic. Inspired by ideas from modular programming and code refactoring, in this work we introduce three kinds of modules: primary, auxiliary, and template, which facilitate the refactoring of a firewall policy into smaller, reusable, comprehensible, and more manageable components. We present algorithms for generating each of the three modules for a given legacy firewall policy. We also develop ModFP, an automated tool for converting legacy firewall policies represented in access control list to their modularized format. With the help of ModFP, when examining several real-world policies with sizes ranging from dozens to hundreds of rules, we were able to identify subtle errors.

Firewall policies are notorious for having misconfiguration errors which can defeat its intended purpose of protecting hosts in the network from malicious users. We believe this is because today's firewall policies are mostly monolithic. Inspired by ideas from modular programming and code refactoring, in this work we introduce three kinds of modules: primary, auxiliary, and template, which facilitate the refactoring of a firewall policy into smaller, reusable, comprehensible, and more manageable components. We present algorithms for generating each of the three modules for a given legacy firewall policy. We also develop ModFP, an automated tool for converting legacy firewall policies represented in access control list to their modularized format. With the help of ModFP, when examining several real-world policies with sizes ranging from dozens to hundreds of rules, we were able to identify subtle errors.

Smartphone users often use private and enterprise data with untrusted third party applications.  The fundamental lack of secrecy guarantees in smartphone OSes, such as Android, exposes this data to the risk of unauthorized exfiltration.  A natural solution is the integration of secrecy guarantees into the OS.  In this paper, we describe the challenges for decentralized information flow control (DIFC) enforcement on Android.  We propose context-sensitive DIFC enforcement via lazy polyinstantiation and practical and secure network export through domain declassification.  Our DIFC system, Weir, is backwards compatible by design, and incurs less than 4 ms overhead for component startup.  With Weir,  we demonstrate practical and secure DIFC enforcement on Android.

When designing driving simulator studies, sometimes high efforts have to be spent to make them successful. Some drivers may not behave as desired, leading to situations unforeseen by the developers. When looking at multi-driver studies, where multiple drivers need to interact with each other in one virtual environment, the probability of performing a successful study is even lower, as the behaviour of the human drivers cannot be fully controlled. While [Oel15b] already proposed guidelines for the creation of such scenarios, this paper describes how the probability of success can be monitored and even enhanced during scenario execution. Therefore, it describes an approach where the probability of success is modelled and where the scenario is dynamically adapted to provide higher rates of success.
 

AbstractCyber assurance of heavy trucks is a major concern with new designs as well as with supporting legacy systems. Many cyber security experts and analysts are used to working with traditional information technology (IT) networks and are familiar with a set of technologies that may not be directly useful in the commercial vehicle sector. To help connect security researchers to heavy trucks, a remotely accessible testbed has been prototyped for experimentation with security methodologies and techniques to evaluate and improve on existing technologies, as well as developing domain-specific technologies. The testbed relies on embedded Linux-based node controllers that can simulate the sensor inputs to various heavy vehicle electronic control units (ECUs). The node controller also monitors and affects the flow of network information between the ECUs and the vehicle communications backbone. For example, a node controller acts as a clone that generates analog wheel speed sensor data while at the same time monitors or controls the network traffic on the J1939 and J1708 networks. The architecture and functions of the node controllers are detailed. Sample interaction with the testbed is illustrated, along with a discussion of the challenges of running remote experiments. Incorporating high fidelity hardware in the testbed enables security researchers to advance the state of the art in hardening heavy vehicle ECUs against cyber-attacks. How the testbed can be used for security research is presented along with an example of its use in evaluating seed/key exchange strength and in intrusion detection systems (IDSs).

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 15 privacy policies from three domains (shopping, telecommunication and social networks) to identify all instances of information type hyponymy. From this dataset, three semantic and four syntactic categories of hyponymy emerged based on category completeness and word-order. Among these, we identified and empirically evaluated 26 Tregex patterns to automate the extraction of hyponyms from privacy policies. The patterns identify information type hypernym-hyponym pairs with an average precision of 0.83 and recall of 0.52 across our dataset of 15 policies. 

Preprocessors support the diversification of software products with #ifdefs, but also require additional effort from developers to maintain and understand variable code. We conjecture that #ifdefs cause developers to produce more vulnerable code because they are required to reason about multiple features simultaneously and maintain complex mental models of dependencies of configurable code.

We extracted a variational call graph across all configurations of the Linux kernel, and used configuration complexity metrics to compare vulnerable and non-vulnerable functions considering their vulnerability history. Our goal was to learn about whether we can observe a measurable influence of configuration complexity on the occurrence of vulnerabilities.

Our results suggest, among others, that vulnerable functions have higher variability than non-vulnerable ones and are also constrained by fewer configuration options. This suggests that developers are inclined to notice functions appear in frequently-compiled product variants. We aim to raise developers' awareness to address variability more systematically, since configuration complexity is an important, but often ignored aspect of software product lines.

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.

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

Software architecture modeling is important for analyzing system quality attributes, particularly security. However, such analyses often assume that the architecture is completely known in advance. In many modern domains, especially those that use plugin-based frameworks, it is not possible to have such a complete model because the software system continuously changes. The Android mobile operating system is one such framework, where users can install and uninstall apps at run time. We need ways to model and analyze such architectures that strike a balance between supporting the dynamism of the underlying platforms and enabling analysis, particularly throughout a system’s lifetime. In this paper, we describe a formal architecture style that captures the modifiable architectures of Android systems, and that supports security analysis as a system evolves. We illustrate the use of the style with two security analyses: a predicatebased approach defined over architectural structure that can detect some common security vulnerabilities, and inter-app permission leakage determined by model checking. We also show how the evolving architecture of an Android device can be obtained by analysis of the apps on a device, and provide some performance evaluation that indicates that the architecture can be amenable for use throughout the system’s lifetime. 

Privacy policies are used to communicate company data practices to consumers and must be accurate and comprehensive. Each policy author is free to use their own nomenclature when describing data practices, which leads to different ways in which similar information types are described across policies. A formal ontology can help policy authors, users and regulators consistently check how data practice descriptions relate to other interpretations of information types. In this paper, we describe an empirical method for manually constructing an information type ontology from privacy policies. The method consists of seven heuristics that explain how to infer hypernym, meronym and synonym relationships from information type phrases, which we discovered using grounded analysis of five privacy policies. The method was evaluated on 50 mobile privacy policies which produced an ontology consisting of 355 unique information type names. Based on the manual results, we describe an automated technique consisting of 14 reusable semantic rules to extract hypernymy, meronymy, and synonymy relations from information type phrases. The technique was evaluated on the manually constructed ontology to yield .95 precision and .51 recall.

The Alloy specification language, and the corresponding Alloy Analyzer, have received much attention in the last two decades with applications in many areas of software engineering. Increasingly, formal analyses enabled by Alloy are desired for use in an on-line mode, where the specifications are automatically kept in sync with the running, possibly changing, software system. However, given Alloy Analyzer’s reliance on computationally expensive SAT solvers, an important challenge is the time it takes for such analyses to execute at runtime. The fact that in an on-line mode, the analyses are often repeated on slightly revised versions of a given specification, presents us with an opportunity to tackle this challenge. We present Titanium, an extension of Alloy for formal analysis of evolving specifications. By leveraging the results from previous analyses, Titanium narrows the state space of the revised specification, thereby greatly reducing the required computational effort. We describe the semantic basis of Titanium in terms of models specified in relational logic. We show how the approach can be realized atop an existing relational logic model finder. Our experimental results show Titanium achieves a significant speed-up over Alloy Analyzer when applied to the analysis of evolving specifications.

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 papers that cover a breadth of security isolation techniques and discuss trade-offs among different design choices and limitations of existing  approaches.

Recent studies have found that parallel garbage collection performs worse with more CPUs and more collector threads. As part of this work, we further investigate this enomenon and find that poor scalability is worst in highly scalable Java applications. Our investigation to find the causes clearly reveals that efficient multi-threading in an application can prolong the average object lifespan, which results in less effective garbage collection. We also find that prolonging lifespan is the direct result of Linux's Completely Fair Scheduler due to its round-robin like behavior that can increase the heap contention between the application threads. Instead, if we use pseudo first-in-first-out to schedule application threads in large multicore systems, the garbage collection scalability is significantly improved while the time spent in garbage collection is reduced by as much as 21%. The average execution time of the 24 Java applications used in our study is also reduced by 11%. Based on this observation, we propose two approaches to optimally select scheduling policies based on application scalability profile. Our first approach uses the profile information from one execution to tune the subsequent executions. Our second approach dynamically collects profile information and performs policy selection during execution.

Conventional security mechanisms at network, host, and source code levels are no longer sufficient in detecting and responding to increasingly dynamic and sophisticated cyber threats today. Detecting anomalous behavior at the architectural level can help better explain the intent of the threat and strengthen overall system security posture. To that end, we present a framework that mines software component interactions from system execution history and applies a detection algorithm to identify anomalous behavior. The framework uses unsupervised learning at runtime, can perform fast anomaly detection “on the fly”, and can quickly adapt to system load fluctuations and user behavior shifts. Our evaluation of the approach against a real Emergency Deployment System has demonstrated very promising results, showing the framework can effectively detect covert attacks, including insider threats, that may be easily missed by traditional intrusion detection methods. 

Our position is that a key component of securing cyber-physical systems (CPS) is to develop a theory of accountability that encompasses both control and computing systems. We envision that a unified theory of accountability in CPS can be built on a foundation of causal information flow analysis. This theory will support design and analysis of mechanisms at various stages of the accountability regime: attack detection, responsibility-assignment (e.g., attack identification or localization), and corrective measures (e.g., via resilient control) As an initial step in this direction, we summarize our results on attack detection in control systems. We use the Kullback-Liebler (KL) divergence as a causal information flow measure. We then recover, using information flow analyses, a set of existing results in the literature that were previously proved using different techniques. These results cover passive detection, stealthy attack characterization, and active detection. This research direction is related to recent work on accountability in computational systems [1], [2], [3], [4]. We envision that by casting accountability theories in computing and control systems in terms of causal information flow, we can provide a common foundation to develop a theory for CPS that compose elements from both domains.