From Russia with Love: Understanding the Russian Cyber Threat to U.S. Critical Infrastructure and What to Do about It

I. Introduction II. A Short History of Russian Hacking of U.S. Government Networks and Critical Infrastructure III. Unpacking the Ukraine Grid Hacks and Their Aftermath IV. Analyzing Policy Options to Help Promote the Resilience of U.S. Government Systems and Critical Infrastructure ... A. Contextualizing and Introducing Draft Version 1.1 of the NIST Cybersecurity Framework ... B. Operationalizing International Cybersecurity Norms on Critical Infrastructure ... C. Deterrence and a Path Forward ... 1. Publicize Benefits as Applied … 2. Publicize Exercise Results ... 3. Publicize Updates V. Conclusio

Improvement of the robustness on geographical networks by adding shortcuts

In a topological structure affected by geographical constraints on liking, the connectivity is weakened by constructing local stubs with small cycles, a something of randomness to bridge them is crucial for the robust network design. In this paper, we numerically investigate the effects of adding shortcuts on the robustness in geographical scale-free network models under a similar degree distribution to the original one. We show that a small fraction of shortcuts is highly contribute to improve the tolerance of connectivity especially for the intentional attacks on hubs. The improvement is equivalent to the effect by fully rewirings without geographical constraints on linking. Even in the realistic Internet topologies, these effects are virtually examined.Comment: 14 pages, 10 figures, 1 tabl

Evaluation of the combined glucose-insulin and intravenous glucose tolerance tests for insulin dysregulation diagnosis in donkeys

Background. Insulin dysregulation (ID) and donkey metabolic syndrome (DMS) are common in this species. Contrary to horses, diagnostic guidelines compiling insulin cut-offs values and dynamic testing interpretations have not been reported for this species. Objectives. To evaluate resting serum insulin concentrations, the combined glucose-insulin test (CGIT) and the glucose intravenous tolerance test (IVGTT) for the diagnosis of DMS with ID suspicion. Study design. Diagnostic test comparison.Methods. Six of 80 mix-breed adult donkeys fulfilled the inclusion criteria for DMS based on history or clinical evidence of recurrent laminitis, body condition >6 and neck score >2 or baseline insulin and leptin concentrations >20 µIU/mL and >12 ng/mL respectively. CGIT and IVGTT were performed in all donkeys within a week and interpreted following guidelines reported for equine metabolic syndrome (EMS). Insulin and glucose curves were analysed, proxies calculated and correlations and multivariate analysis assessed. Results. Following EMS guidelines, CGIT classified 2 (using glucose-positive phase duration) or 3 (using insulin concentration) and IVGTT classified 5 donkeys as ID. ID donkeys showed a lower glucose/insulin ratio, QUICKI and RISQI, and a higher insulin/glucose ratio, MIRG and HOMA-B%. Main limitations. Comparison of these tests with additional dynamic testing including a larger number of ID donkeys is necessary. Conclusions. This is the first study evaluating dynamic tests to assess ID/DMS in DMS-suspected donkeys. IVGTT detected more ID donkeys than CGIT. EMS recommendations could also be used for DMS diagnosis, although a baseline insulin cut-off value is needed

Using Social Media & Sentiment Analysis to Make Investment Decisions

Making investment decisions by utilizing sentiment data from social media (SM) is starting to become a more tangible concept. There has been a broad investigation into this field of study over the last decade, and many of the findings have promising results. However, there is still an opportunity for continued research, firstly, in finding the most effective way to obtain relevant sentiment data from SM, then building a system to measure the sentiment, and finally visualizing it to help users make investment decisions. Furthermore, much of the existing work fails to factor SM metrics into the sentiment score effectively. This paper presents a novel prototype as a contribution to the field of study. In our work, a detailed overview of the topic is given in the form of a literature and technical review. Next, a prototype is designed and developed using the findings from the previous analysis. On top of that, a novel approach to factor SM metrics into the sentiment score is presented, with the goal of measuring the collective sentiment of the data effectively. To test the proposed approach, we only used popular stocks from the S&P500 to ensure large volumes of SM sentiment was available, adding further insight into findings, which we then discuss in our evaluation

Making Democracy Harder to Hack

With the Russian government hack of the Democratic National Convention email servers and related leaks, the drama of the 2016 U.S. presidential race highlights an important point: nefarious hackers do not just pose a risk to vulnerable companies; cyber attacks can potentially impact the trajectory of democracies. Yet a consensus has been slow to emerge as to the desirability and feasibility of reclassifying elections—in particular, voting machines—as critical infrastructure, due in part to the long history of local and state control of voting procedures. This Article takes on the debate—focusing on policy options beyond former Department of Homeland Security Secretary Jeh Johnson’s decision to classify elections as critical infrastructure in January 2017—in the U.S., using the 2016 elections as a case study, but putting the issue in a global context, with in-depth case studies from South Africa, Estonia, Brazil, Germany, and India. Governance best practices are analyzed by reviewing these differing approaches to securing elections, including the extent to which trend lines are converging or diverging. This investigation will, in turn, help inform ongoing minilateral efforts at cybersecurity norm building in the critical infrastructure context, which are considered here for the first time in the literature through the lens of polycentric governance

Gaussian Process Regression models for the properties of micro-tearing modes in spherical tokamak

Spherical tokamaks (STs) have many desirable features that make them an attractive choice for a future fusion power plant. Power plant viability is intrinsically related to plasma heat and particle confinement and this is often determined by the level of micro-instability driven turbulence. Accurate calculation of the properties of turbulent micro-instabilities is therefore critical for tokamak design, however, the evaluation of these properties is computationally expensive. The considerable number of geometric and thermodynamic parameters and the high resolutions required to accurately resolve these instabilities makes repeated use of direct numerical simulations in integrated modelling workflows extremely computationally challenging and creates the need for fast, accurate, reduced-order models. This paper outlines the development of a data-driven reduced-order model, often termed a {\it surrogate model} for the properties of micro-tearing modes (MTMs) across a spherical tokamak reactor-relevant parameter space utilising Gaussian Process Regression (GPR) and classification; techniques from machine learning. These two components are used in an active learning loop to maximise the efficiency of data acquisition thus minimising computational cost. The high-fidelity gyrokinetic code GS2 is used to calculate the linear properties of the MTMs: the mode growth rate, frequency and normalised electron heat flux; core components of a quasi-linear transport model. Five-fold cross-validation and direct validation on unseen data is used to ascertain the performance of the resulting surrogate models

The question of energy reduction: The problem(s) with feedback

With smart metering initiatives gaining increasing global popularity, the present paper seeks to challenge the increasingly entrenched view that providing householders with feedback about their energy usage, via an in-home-display, will lead them to substantially reduce their energy consumption. Specifically, we draw on existing quantitative and qualitative evidence to outline three key problems with feedback, namely: (a) the limited evidence of efficacy, (b) the need for user engagement, and (c) the potential for unintended consequences. We conclude by noting that, in their current form, existing in-home-displays may not induce the desired energy-reduction response anticipated by smart metering initiatives. Instead, if smart metering is to effectively reduce energy consumption there is a clear need to develop and test innovative new feedback devices that have been designed with user engagement in mind

Equilibrium statistical mechanics on correlated random graphs

Biological and social networks have recently attracted enormous attention between physicists. Among several, two main aspects may be stressed: A non trivial topology of the graph describing the mutual interactions between agents exists and/or, typically, such interactions are essentially (weighted) imitative. Despite such aspects are widely accepted and empirically confirmed, the schemes currently exploited in order to generate the expected topology are based on a-priori assumptions and in most cases still implement constant intensities for links. Here we propose a simple shift in the definition of patterns in an Hopfield model to convert frustration into dilution: By varying the bias of the pattern distribution, the network topology -which is generated by the reciprocal affinities among agents - crosses various well known regimes (fully connected, linearly diverging connectivity, extreme dilution scenario, no network), coupled with small world properties, which, in this context, are emergent and no longer imposed a-priori. The model is investigated at first focusing on these topological properties of the emergent network, then its thermodynamics is analytically solved (at a replica symmetric level) by extending the double stochastic stability technique, and presented together with its fluctuation theory for a picture of criticality. At least at equilibrium, dilution simply decreases the strength of the coupling felt by the spins, but leaves the paramagnetic/ferromagnetic flavors unchanged. The main difference with respect to previous investigations and a naive picture is that within our approach replicas do not appear: instead of (multi)-overlaps as order parameters, we introduce a class of magnetizations on all the possible sub-graphs belonging to the main one investigated: As a consequence, for these objects a closure for a self-consistent relation is achieved.Comment: 30 pages, 4 figure

Alterations in intestinal microbiota of children with celiac disease at time of diagnosis and on a gluten-free diet

Background & Aims: It is not clear whether alterations in the intestinal microbiota of children with celiac disease cause the disease or are a result of disease and/or its treatment with gluten-free diet (GFD). Methods: We obtained 167 fecal samples from 141 children (20 with new-onset celiac disease, 45 treated with a GFD, 57 healthy children, and 19 unaffected siblings of children with celiac disease) in Glasgow, Scotland. Samples were analyzed by 16S rRNA sequencing and diet-related metabolites were measured by gas chromatography. We obtained fecal samples from 13 of the children with new-onset CD after 6 and 12 months on GFD. Relationships between microbiota with diet composition, gastrointestinal function, and biomarkers of GFD compliance were explored. Results: Microbiota α diversity did not differ among groups. Microbial dysbiosis was not observed in children with new-onset celiac disease. In contrast, 2.8% (Bray-Curtis dissimilarity index, P=.025) and 2.5% (UniFrac distances, P=.027) of the variation in microbiota composition could be accounted for by the GFD. Between 3% to 5% of all taxa differed among all group comparisons. Eleven distinctive operational taxonomic units composed a microbe signature specific to celiac disease with high diagnostic probability. Most of the operational taxonomic units that differed between patients on GFD with new-onset celiac disease vs healthy children were associated with nutrient and food group intake (from 75% to 94%), and with biomarkers of gluten ingestion. Fecal levels of butyrate and ammonia decreased during the GFD. Conclusions: Although several alterations in the intestinal microbiota of children with established celiac disease appear to be effects of a GFD, there are specific bacteria that are distinct biomarkers of celiac disease. Studies are needed to determine whether these bacteria contribute to pathogenesis of celiac disease

Junior Recital: Andrew J. Yi, Percussion

This recital is presented in partial fulfillment of requirements for the degree Bachelor of Music in Music Performance. Mr. Yi studies percussion with John Lawless.https://digitalcommons.kennesaw.edu/musicprograms/2286/thumbnail.jp