Biblio

The explosive growth of IT infrastructures, cloud systems, and Internet of Things (IoT) have resulted in complex systems that are extremely difficult to secure and protect against cyberattacks which are growing exponentially in complexity and in number. Overcoming the cybersecurity challenges is even more complicated due to the lack of training and widely available cybersecurity environments to experiment with and evaluate new cybersecurity methods. The goal of our research is to address these challenges by exploiting cloud services. In this paper, we present the design, analysis, and evaluation of a cloud service that we refer to as Cybersecurity Lab as a Service (CLaaS) which offers virtual cybersecurity experiments that can be accessed from anywhere and from any device (desktop, laptop, tablet, smart mobile device, etc.) with Internet connectivity. In CLaaS, we exploit cloud computing systems and virtualization technologies to provide virtual cybersecurity experiments and hands-on experiences on how vulnerabilities are exploited to launch cyberattacks, how they can be removed, and how cyber resources and services can be hardened or better protected. We also present our experimental results and evaluation of CLaaS virtual cybersecurity experiments that have been used by graduate students taking our cybersecurity class as well as by high school students participating in GenCyber camps.

Security subsystems are often designed with flawed assumptions arising from system designers' faulty mental models. Designers tend to assume that users behave according to some textbook ideal, and to consider each potential exposure/interface in isolation. However, fieldwork continually shows that even well-intentioned users often depart from this ideal and circumvent controls in order to perform daily work tasks, and that "incorrect" user behaviors can create unexpected links between otherwise "independent" interfaces. When it comes to security features and parameters, designers try to find the choices that optimize security utility–-except these flawed assumptions give rise to an incorrect curve, and lead to choices that actually make security worse, in practice. We propose that improving this situation requires giving designers more accurate models of real user behavior and how it influences aggregate system security. Agent-based modeling can be a fruitful first step here. In this paper, we study a particular instance of this problem, propose user-centric techniques designed to strengthen the security of systems while simultaneously improving the usability of them, and propose further directions of inquiry.

This paper examines security faults/vulnerabilities reported for Fedora. Results indicate that, at least in some situations, fault roughly constant may be used to guide estimation of residual vulnerabilities in an already released product, as well as possibly guide testing of the next version of the product.

Applied Cyber-Physical Systems presents the latest methods and technologies in the area of cyber-physical systems including medical and biological applications. Cyber-physical systems (CPS) integrate computing and communication capabilities by monitoring, and controlling the physical systems via embedded hardware and computers.

This book brings together unique contributions from renowned experts on cyber-physical systems research and education with applications. It also addresses the major challenges in CPS, and then provides a resolution with various diverse applications as examples.

Advanced-level students and researchers focused on computer science, engineering and biomedicine will find this to be a useful secondary text book or reference, as will professionals working in this field.

Security features are often hardwired into software applications, making it difficult to adapt security responses to reflect changes in runtime context and new attacks. In prior work, we proposed the idea of architecture-based self-protection as a way of separating adaptation logic from application logic and providing a global perspective for reasoning about security adaptations in the context of other business goals. In this paper, we present an approach, based on this idea, for combating denial-of-service (DoS) attacks. Our approach allows DoS-related tactics to be composed into more sophisticated mitigation strategies that encapsulate possible responses to a security problem. Then, utility-based reasoning can be used to consider different business contexts and qualities. We describe how this approach forms the underpinnings of a scientific approach to self-protection, allowing us to reason about how to make the best choice of mitigation at runtime. Moreover, we also show how formal analysis can be used to determine whether the mitigations cover the range of conditions the system is likely to encounter, and the effect of mitigations on other quality attributes of the system. We evaluate the approach using the Rainbow self-adaptive framework and show how Rainbow chooses DoS mitigation tactics that are sensitive to different business contexts.

An authenticated data structure (ADS) is a data structure whose operations can be carried out by an untrusted prover, the results of which a verifier can efficiently check as authentic. This is done by having the prover produce a compact proof that the verifier can check along with each operation's result. ADSs thus support outsourcing data maintenance and processing tasks to untrusted servers without loss of integrity. Past work on ADSs has focused on particular data structures (or limited classes of data structures), one at a time, often with support only for particular operations.

This paper presents a generic method, using a simple extension to a ML-like functional programming language we call λ• (lambda-auth), with which one can program authenticated operations over any data structure defined by standard type constructors, including recursive types, sums, and products. The programmer writes the data structure largely as usual and it is compiled to code to be run by the prover and verifier. Using a formalization of λ• we prove that all well-typed λ• programs result in code that is secure under the standard cryptographic assumption of collision-resistant hash functions. We have implemented λ• as an extension to the OCaml compiler, and have used it to produce authenticated versions of many interesting data structures including binary search trees, red-black+ trees, skip lists, and more. Performance experiments show that our approach is efficient, giving up little compared to the hand-optimized data structures developed previously.

In this paper, it is shown that the high automation level of the object-oriented modeling paradigm for physical systems can significantly rationalize the design procedure of fault detection and isolation (FDI) systems. Consequently, an object-oriented FDI method for complex engineering systems consisting of subsystems from different physical domains like mechatronic systems, commercial vehicles, and chemical process plants is developed. The mathematical composition of the objects corresponding to the subsystems results in a differential algebraic equation (DAE) that describes the overall system. This DAE is automatically analyzed and transferred into a set of residual generators that enable a two-stage FDI procedure for multiple fault modes.
 

Cloud storage has rapidly acquired popularity among users, constituting a seamless solution for the backup, synchronization, and sharing of large amounts of data. This technology, however, puts user data in the direct control of cloud service providers, which raises increasing security and privacy concerns related to the integrity of outsourced data, the accidental or intentional leakage of sensitive information, the profiling of user activities and so on. We present GORAM, a cryptographic system that protects the secrecy and integrity of the data outsourced to an untrusted server and guarantees the anonymity and unlinkability of consecutive accesses to such data. GORAM allows the database owner to share outsourced data with other clients, selectively granting them read and write permissions. GORAM is the first system to achieve such a wide range of security and privacy properties for outsourced storage. Technically, GORAM builds on a combination of ORAM to conceal data accesses, attribute-based encryption to rule the access to outsourced data, and zero-knowledge proofs to prove read and write permissions in a privacy-preserving manner. We implemented GORAM and conducted an experimental evaluation to demonstrate its feasibility.

It is widely accepted that wireless channels decorrelate fast over space, and half a wavelength is the key distance metric used in link signature (LS) for security assurance. However, we believe that this channel correlation model is questionable, and will lead to false sense of security. In this project, we focus on establishing correct modeling of channel correlation so as to facilitate proper guard zone designs for LS security in various wireless environments of interest.

Coactive Design is a new approach to address the increasingly sophisticated roles that people and robots play as the use of robots expands into new, complex domains. The approach is motivated by the desire for robots to perform less like teleoperated tools or independent automatons and more like interdependent teammates. In this article, we describe what it means to be interdependent, why this is important, and the design implications that follow from this perspective. We argue for a human-robot system model that supports interdependence through careful attention to requirements for observability, predictability, and directability. We present a Coactive Design method and show how it can be a useful approach for developers trying to understand how to translate high-level teamwork concepts into reusable control algorithms, interface elements, and behaviors that enable robots to fulfill their envisioned role as teammates. As an example of the coactive design approach, we present our results from the DARPA Virtual Robotics Challenge, a competition designed to spur development of advanced robots that can assist humans in recovering from natural and man-made disasters. Twenty-six teams from eight countries competed in three different tasks providing an excellent evaluation of the relative effectiveness of different approaches to human-machine system design.

Trust relationships occur naturally in many diverse contexts such as collaborative systems, e-commerce, interpersonal interactions, social networks, and semantic sensor web. As agents providing content and services become increasingly removed from the agents that consume them, the issue of robust trust inference and update becomes critical. There is a need to find online substitutes for traditional (direct or face-to-face) cues to derive measures of trust, and create efficient and robust systems for managing trust in order to support decision-making. Unfortunately, there is neither a universal notion of trust that is applicable to all domains nor a clear explication of its semantics or computation in many situations. We motivate the trust problem, explain the relevant concepts, summarize research in modeling trust and gleaning trustworthiness, and discuss challenges confronting us. The goal is to provide a comprehensive broad overview of the trust landscape, with the nitty-gritties of a handful of approaches. We also provide details of the theoretical underpinnings and comparative analysis of Bayesian approaches to binary and multi-level trust, to automatically determine trustworthiness in a variety of reputation systems including those used in sensor networks, e-commerce, and collaborative environments. Ultimately, we need to develop expressive trust networks that can be assigned objective semantics.

This paper presents an evaluation of various methodologies used to determine relative significances of input variables in data-driven models. Significance analysis applied to manufacturing process parameters can be a useful tool in fault diagnosis for various types of manufacturing processes. It can also be applied to building models that are used in process control. The relative significances of input variables can be determined by various data mining methods, including relatively simple statistical procedures as well as more advanced machine learning systems. Several methodologies suitable for carrying out classification tasks which are characteristic of fault diagnosis were evaluated and compared from the viewpoint of their accuracy, robustness of results and applicability. Two types of testing data were used: synthetic data with assumed dependencies and real data obtained from the foundry industry. The simple statistical method based on contingency tables revealed the best overall performance, whereas advanced machine learning models, such as ANNs and SVMs, appeared to be of less value.

We consider the problem of cross-layer resource allocation with information-theoretic secrecy for uplink transmissions in time-varying cellular wireless networks. Particularly, each node in an uplink cellular network injects two types of traffic, confidential and open at rates chosen in order to maximize a global utility function while keeping the data queues stable and meeting a constraint on the secrecy outage probability. The transmitting node only knows the distribution of channel gains. Our scheme is based on Hybrid Automatic Repeat Request (HARQ) transmission with incremental redundancy. We prove that our scheme achieves a utility, arbitrarily close to the maximum achievable. Numerical experiments are performed to verify the analytical results and to show the efficacy of the dynamic control algorithm.
 

Static types may be used both by the language implementation and directly by the user as documentation. Though much existing work focuses primarily on the implications of static types on the semantics of programs, relatively little work considers the impact on usability that static types pro- vide. Though the omission of static type information may decrease program length and thereby improve readability, it may also decrease readability because users must then frequently derive type information manually while reading programs. As type inference becomes more popular in languages that are in widespread use, it is important to consider whether the adoption of type inference may impact productivity of developers.

Individuals sharing information can improve the cost or performance of a distributed control system. But, sharing may also violate privacy. We develop a general framework for studying the cost of differential privacy in systems where a collection of agents, with coupled dynamics, communicate for sensing their shared environment while pursuing individ- ual preferences. First, we propose a communication strategy that relies on adding carefully chosen random noise to agent states and show that it preserves differential privacy. Of course, the higher the standard deviation of the noise, the higher the cost of privacy. For linear distributed control systems with quadratic cost functions, the standard deviation becomes independent of the number agents and it decays with the maximum eigenvalue of the dynamics matrix. Furthermore, for stable dynamics, the noise to be added is independent of the number of agents as well as the time horizon up to which privacy is desired.

Current Trusted Platform Modules (TPMs) are illsuited for cross-device scenarios in trusted mobile applications because they hinder the seamless sharing of data across multiple devices. This paper presents cTPM, an extension of the TPM's design that adds an additional root key to the TPM and shares that root key with the cloud. As a result, the cloud can create and share TPM-protected keys and data across multiple devices owned by one user. Further, the additional key lets the cTPM allocate cloud-backed remote storage so that each TPM can benefit from a trusted real-time clock and high-performance, non-volatile storage.

This paper shows that cTPM is practical, versatile, and easily applicable to trusted mobile applications. Our simple change to the TPM specification is viable because its fundamental concepts - a primary root key and off-chip, NV storage - are already found in the current specification, TPM 2.0. By avoiding a clean-slate redesign, we sidestep the difficult challenge of re-verifying the security properties of a new TPM design. We demonstrate cTPM's versatility with two case studies: extending Pasture with additional functionality, and reimplementing TrInc without the need for extra hardware.

As smart meters continue to be deployed around the world collecting unprecedented levels of fine-grained data about consumers, we need to find mechanisms that are fair to both, (1) the electric utility who needs the data to improve their operations, and (2) the consumer who has a valuation of privacy but at the same time benefits from sharing consumption data. In this paper we address this problem by proposing privacy contracts between electric utilities and consumers with the goal of maximizing the social welfare of both. Our mathematical model designs an optimization problem between a population of users that have different valuations on privacy and the costs of operation by the utility. We then show how contracts can change depending on the probability of a privacy breach. This line of research can help inform not only current but also future smart meter collection practices.