On Improving Robustness of Hardware Security Primitives and Resistance to Reverse Engineering Attacks

The continued growth of information technology (IT) industry and proliferation of interconnected devices has aggravated the problem of ensuring security and necessitated the need for novel, robust solutions. Physically unclonable functions (PUFs) have emerged as promising secure hardware primitives that can utilize the disorder introduced during manufacturing process to generate unique keys. They can be utilized as \textit{lightweight} roots-of-trust for use in authentication and key generation systems. Unlike insecure non-volatile memory (NVM) based key storage systems, PUFs provide an advantage -- no party, including the manufacturer, should be able to replicate the physical disorder and thus, effectively clone the PUF. However, certain practical problems impeded the widespread deployment of PUFs. This dissertation addresses such problems of (i) reliability and (ii) unclonability. Also, obfuscation techniques have proven necessary to protect intellectual property in the presence of an untrusted supply chain and are needed to aid against counterfeiting. This dissertation explores techniques utilizing layout and logic-aware obfuscation. Collectively, we present secure and cost-effective solutions to address crucial hardware security problems

FireDeX: a Prioritized IoT Data Exchange Middleware for Emergency Response

International audienceReal-time event detection and targeted decision making for emerging mission-critical applications, e.g. smart fire fighting, requires systems that extract and process relevant data from connected IoT devices in the environment. In this paper, we propose FireDeX, a cross-layer middleware that facilitates timely and effective exchange of data for coordinating emergency response activities. FireDeX adopts a publish-subscribe data exchange paradigm with brokers at the network edge to manage prioritized delivery of mission-critical data from IoT sources to relevant subscribers. It incorporates parameters at the application, network, and middleware layers into a data exchange service that accurately estimates end-to-end performance metrics (e.g. delays, success rates). We design an extensible queueing theoretic model that abstracts these cross-layer interactions as a network of queues, thereby making it amenable for rapid analysis. We propose novel algorithms that utilize results of this analysis to tune data exchange configurations (event priorities and dropping policies) while meeting situational awareness requirements and resource constraints. FireDeX leverages Software-Defined Networking (SDN) methodologies to enforce these configurations in the IoT network infrastructure. We evaluate its performance through simulated experiments in a smart building fire response scenario. Our results demonstrate significant improvement to mission-critical data delivery under a variety of conditions. Our application-aware prioritization algorithm improves the value of exchanged information by 36% when compared with no prioritization; the addition of our network-aware drop rate policies improves this performance by 42% over priorities only and by 94% over no prioritization

Parallel computing for brain simulation

[Abstract] Background: The human brain is the most complex system in the known universe, it is therefore one of the greatest mysteries. It provides human beings with extraordinary abilities. However, until now it has not been understood yet how and why most of these abilities are produced. Aims: For decades, researchers have been trying to make computers reproduce these abilities, focusing on both understanding the nervous system and, on processing data in a more efficient way than before. Their aim is to make computers process information similarly to the brain. Important technological developments and vast multidisciplinary projects have allowed creating the first simulation with a number of neurons similar to that of a human brain. Conclusion: This paper presents an up-to-date review about the main research projects that are trying to simulate and/or emulate the human brain. They employ different types of computational models using parallel computing: digital models, analog models and hybrid models. This review includes the current applications of these works, as well as future trends. It is focused on various works that look for advanced progress in Neuroscience and still others which seek new discoveries in Computer Science (neuromorphic hardware, machine learning techniques). Their most outstanding characteristics are summarized and the latest advances and future plans are presented. In addition, this review points out the importance of considering not only neurons: Computational models of the brain should also include glial cells, given the proven importance of astrocytes in information processing.Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; GRC2014/049Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; R2014/039Instituto de Salud Carlos III; PI13/0028

[Activity of Institute for Computer Applications in Science and Engineering]

This report summarizes research conducted at the Institute for Computer Applications in Science and Engineering in applied mathematics, fluid mechanics, and computer science

Calibration and Analysis of Enterprise and Edge Network Measurements

With the growth of the Internet over the past several decades, the field of Internet and network measurements has attracted the attention of many researchers. Doing the measurements has allowed a better understanding of the inner workings of both the global Internet and its specific parts. But undertaking a measurement study in a sound fashion is no easy task. Given the complexity of modern networks, one has to take great care in anticipating, detecting and eliminating all the measurement errors and biases. In this thesis we pave the way for a more systematic calibration of network traces. Such calibration ensures the soundness and robustness of the analysis results by revealing and fixing flaws in the data. We collect our measurement data in two environments: in a medium-sized enterprise and at the Internet edge. For the former we perform two rounds of data collection from the enterprise switches. We use the differences in the way we recorded the network traces during the first and second rounds to develop and assess the methodology for five calibration aspects: measurement gain, measurement loss, measurement reordering, timing, and topology. For the dataset gathered at the Internet edge, we perform calibration in the form of extensive checks of data consistency and sanity. After calibrating the data, we engage in the analysis of its various aspects. For the enterprise dataset we look at TCP dynamics in the enterprise environment. Here we first make a high- level overview of TCP connection characteristics such as termination status, size, duration, rate, etc. Then we assess the parameters important for TCP performance, such as retransmissions, out-of-order deliveries and channel utilization. Finally, using the Internet edge dataset, we gauge the performance characteristics of the edge connectivity

A review of machine learning applications in wildfire science and management

Artificial intelligence has been applied in wildfire science and management since the 1990s, with early applications including neural networks and expert systems. Since then the field has rapidly progressed congruently with the wide adoption of machine learning (ML) in the environmental sciences. Here, we present a scoping review of ML in wildfire science and management. Our objective is to improve awareness of ML among wildfire scientists and managers, as well as illustrate the challenging range of problems in wildfire science available to data scientists. We first present an overview of popular ML approaches used in wildfire science to date, and then review their use in wildfire science within six problem domains: 1) fuels characterization, fire detection, and mapping; 2) fire weather and climate change; 3) fire occurrence, susceptibility, and risk; 4) fire behavior prediction; 5) fire effects; and 6) fire management. We also discuss the advantages and limitations of various ML approaches and identify opportunities for future advances in wildfire science and management within a data science context. We identified 298 relevant publications, where the most frequently used ML methods included random forests, MaxEnt, artificial neural networks, decision trees, support vector machines, and genetic algorithms. There exists opportunities to apply more current ML methods (e.g., deep learning and agent based learning) in wildfire science. However, despite the ability of ML models to learn on their own, expertise in wildfire science is necessary to ensure realistic modelling of fire processes across multiple scales, while the complexity of some ML methods requires sophisticated knowledge for their application. Finally, we stress that the wildfire research and management community plays an active role in providing relevant, high quality data for use by practitioners of ML methods.Comment: 83 pages, 4 figures, 3 table

Discrete Event Simulations

Considered by many authors as a technique for modelling stochastic, dynamic and discretely evolving systems, this technique has gained widespread acceptance among the practitioners who want to represent and improve complex systems. Since DES is a technique applied in incredibly different areas, this book reflects many different points of view about DES, thus, all authors describe how it is understood and applied within their context of work, providing an extensive understanding of what DES is. It can be said that the name of the book itself reflects the plurality that these points of view represent. The book embraces a number of topics covering theory, methods and applications to a wide range of sectors and problem areas that have been categorised into five groups. As well as the previously explained variety of points of view concerning DES, there is one additional thing to remark about this book: its richness when talking about actual data or actual data based analysis. When most academic areas are lacking application cases, roughly the half part of the chapters included in this book deal with actual problems or at least are based on actual data. Thus, the editor firmly believes that this book will be interesting for both beginners and practitioners in the area of DES

Effects of riparian grazing on terrestrial invertebrate subsidies that feed trout in central Rocky Mountain streams

2010 Fall.Includes bibliographic references (pages 198-207).Habitat degradation is the leading cause of biodiversity loss worldwide, affecting plant and animal populations directly through habitat loss, but also indirectly by decoupling important linkages among habitats. Linkages between streams and the terrestrial environments they drain are likely to be especially important because streams have small habitat area but long boundaries with the adjacent riparian zone. Riparian livestock grazing reduces riparian vegetation, altering the stream-riparian interface, and so may reduce the flux of terrestrial invertebrates to streams. To evaluate the potential for riparian grazing to affect trout populations by reducing this flux, I conducted two large-scale field studies. In the first, a study of three commonly used grazing systems at sites on 16 streams in northern Colorado, I compared invertebrate resources and salmonid populations among stream reaches managed for season-long (i.e., continuous) or two types of rotational livestock grazing, as well as streams grazed by wildlife only. Rotational grazing generally supported greater inputs of terrestrial invertebrates to streams (2-5 times more), and trout at rotational grazing sites consumed 2 - 4 times the biomass of these prey as trout at sites grazed season-long. However, factors influencing the flux of invertebrates to streams were complex and resulted in variable responses by trout populations. In the second field study, a large-scale grazing experiment conducted in four streams in western Wyoming, I evaluated whether two levels of grazing intensity (i.e., the amount of vegetation removed) and manual removal of streamside woody vegetation influenced terrestrial prey resources for trout when compared to controls with wildlife grazing only. Two grazing treatments, designed to reduce vegetation to 10-15-cm stubble height (moderate intensity grazing) or 5-7.5-cm stubble height (high intensity grazing) within a few days, had no detectable effect on terrestrial invertebrates entering streams, whereas high intensity grazing combined with manual removal of two-thirds of streamside woody vegetation reduced inputs of terrestrial invertebrates to streams by 45%. In contrast, all treatments reduced the biomass of these prey in tout diets by 50 - 75%, relative to control sites. However, neither grazing nor removal of woody vegetation affected the biomass of fish that remained in treatment reaches. Finally, I conducted field research and computer simulations to validate removal estimates of trout abundance, based on night-time electrofishing, to address recent concerns over the accuracy of these types of estimators. I found that night-time electrofishing was highly effective for estimating abundance of trout in small streams like those where I studied the effects of cattle grazing in Colorado and Wyoming. Furthermore, I show that modern analytical methods provide powerful tools to account for heterogeneity in capture probability among individual fish

Virginia Institute of Marine Science Fortieth Annual Report for the Period Ending 30 June 1981

https://scholarworks.wm.edu/vimsannualrpt/1017/thumbnail.jp