Using Machine Learning to Assist with the Selection of Security Controls During Security Assessment

In many domains such as healthcare and banking, IT systems need to fulfill various requirements related to security. The elaboration of security requirements for a given system is in part guided by the controls envisaged by the applicable security standards and best practices. An important difficulty that analysts have to contend with during security requirements elaboration is sifting through a large number of security controls and determining which ones have a bearing on the security requirements for a given system. This challenge is often exacerbated by the scarce security expertise available in most organizations. [Objective] In this article, we develop automated decision support for the identification of security controls that are relevant to a specific system in a particular context. [Method and Results] Our approach, which is based on machine learning, leverages historical data from security assessments performed over past systems in order to recommend security controls for a new system. We operationalize and empirically evaluate our approach using real historical data from the banking domain. Our results show that, when one excludes security controls that are rare in the historical data, our approach has an average recall of ≈ 94% and average precision of ≈ 63%. We further examine through a survey the perceptions of security analysts about the usefulness of the classification models derived from historical data. [Conclusions] The high recall – indicating only a few relevant security controls are missed – combined with the reasonable level of precision – indicating that the effort required to confirm recommendations is not excessive – suggests that our approach is a useful aid to analysts for more efficiently identifying the relevant security controls, and also for decreasing the likelihood that important controls would be overlooked. Further, our survey results suggest that the generated classification models help provide a documented and explicit rationale for choosing the applicable security controls

Heterogeneous Bond Percolation on Multitype Networks with an Application to Epidemic Dynamics

Considerable attention has been paid, in recent years, to the use of networks in modeling complex real-world systems. Among the many dynamical processes involving networks, propagation processes -- in which final state can be obtained by studying the underlying network percolation properties -- have raised formidable interest. In this paper, we present a bond percolation model of multitype networks with an arbitrary joint degree distribution that allows heterogeneity in the edge occupation probability. As previously demonstrated, the multitype approach allows many non-trivial mixing patterns such as assortativity and clustering between nodes. We derive a number of useful statistical properties of multitype networks as well as a general phase transition criterion. We also demonstrate that a number of previous models based on probability generating functions are special cases of the proposed formalism. We further show that the multitype approach, by naturally allowing heterogeneity in the bond occupation probability, overcomes some of the correlation issues encountered by previous models. We illustrate this point in the context of contact network epidemiology.Comment: 10 pages, 5 figures. Minor modifications were made in figures 3, 4 and 5 and in the text. Explanations and references were adde

Fine-Scale Population Recombination Rates, Hotspots, and Correlates of Recombination in the Medicago truncatula Genome

Recombination rates vary across the genome and in many species show significant relationships with several genomic features, including distance to the centromere, gene density, and GC content. Studies of fine-scale recombination rates have also revealed that in several species, there are recombination hotspots, that is, short regions with recombination rates 10–100 greater than those in surrounding regions. In this study, we analyzed whole-genome resequence data from 26 accessions of the model legume Medicago truncatula to gain insight into the genomic features that are related to high- and low-recombination rates and recombination hotspots at 1 kb scales. We found that high-recombination regions (1-kb windows among those in the highest 5% of the distribution) on all three chromosomes were significantly closer to the centromere, had higher gene density, and lower GC content than low-recombination windows. High-recombination windows are also significantly overrepresented among some gene functional categories—most strongly NB–ARC and LRR genes, both of which are important in plant defense against pathogens. Similar to high-recombination windows, recombination hotspots (1-kb windows with significantly higher recombination than the surrounding region) are significantly nearer to the centromere than nonhotspot windows. By contrast, we detected no difference in gene density or GC content between hotspot and nonhotspot windows. Using linear model wavelet analysis to examine the relationship between recombination and genomic features across multiple spatial scales, we find a significant negative correlation with distance to the centromere across scales up to 512 kb, whereas gene density and GC content show significantly positive and negative correlations, respectively, only up to 64 kb. Correlations between recombination and genomic features, particularly gene density and polymorphism, suggest that they are scale dependent and need to be assessed at scales relevant to the evolution of those features

Ultrashort filaments of light in weakly-ionized, optically-transparent media

Modern laser sources nowadays deliver ultrashort light pulses reaching few cycles in duration, high energies beyond the Joule level and peak powers exceeding several terawatt (TW). When such pulses propagate through optically-transparent media, they first self-focus in space and grow in intensity, until they generate a tenuous plasma by photo-ionization. For free electron densities and beam intensities below their breakdown limits, these pulses evolve as self-guided objects, resulting from successive equilibria between the Kerr focusing process, the chromatic dispersion of the medium, and the defocusing action of the electron plasma. Discovered one decade ago, this self-channeling mechanism reveals a new physics, widely extending the frontiers of nonlinear optics. Implications include long-distance propagation of TW beams in the atmosphere, supercontinuum emission, pulse shortening as well as high-order harmonic generation. This review presents the landmarks of the 10-odd-year progress in this field. Particular emphasis is laid to the theoretical modeling of the propagation equations, whose physical ingredients are discussed from numerical simulations. Differences between femtosecond pulses propagating in gaseous or condensed materials are underlined. Attention is also paid to the multifilamentation instability of broad, powerful beams, breaking up the energy distribution into small-scale cells along the optical path. The robustness of the resulting filaments in adverse weathers, their large conical emission exploited for multipollutant remote sensing, nonlinear spectroscopy, and the possibility to guide electric discharges in air are finally addressed on the basis of experimental results.Comment: 50 pages, 38 figure

Formate overflow drives toxic folate trapping in MTHFD1 inhibited cancer cells

Cancer cells fuel their increased need for nucleotide supply by upregulating one-carbon (1C) metabolism, including the enzymes methylenetetrahydrofolate dehydrogenase-cyclohydrolase 1 and 2 (MTHFD1 and MTHFD2). TH9619 is a potent inhibitor of dehydrogenase and cyclohydrolase activities in both MTHFD1 and MTHFD2, and selectively kills cancer cells. Here, we reveal that, in cells, TH9619 targets nuclear MTHFD2 but does not inhibit mitochondrial MTHFD2. Hence, overflow of formate from mitochondria continues in the presence of TH9619. TH9619 inhibits the activity of MTHFD1 occurring downstream of mitochondrial formate release, leading to the accumulation of 10-formyl-tetrahydrofolate, which we term a 'folate trap'. This results in thymidylate depletion and death of MTHFD2-expressing cancer cells. This previously uncharacterized folate trapping mechanism is exacerbated by physiological hypoxanthine levels that block the de novo purine synthesis pathway, and additionally prevent 10-formyl-tetrahydrofolate consumption for purine synthesis. The folate trapping mechanism described here for TH9619 differs from other MTHFD1/2 inhibitors and antifolates. Thus, our findings uncover an approach to attack cancer and reveal a regulatory mechanism in 1C metabolism.In this study, Green, Marttila, Kiweler et al. characterize one-carbon metabolism rewiring in response to a dual MTHFD1 and MTHFD2 inhibitor. This work provides insight into one-carbon fluxes, and reveals a previously uncharacterized vulnerability in cancer cells created by folate trapping

Dynamic biogeochemical provinces in the global ocean

In recent decades, it has been found useful to partition the pelagic environment using the concept of biogeochemical provinces, or BGCPs, within each of which it is assumed that environmental conditions are distinguishable and unique at global scale. The boundaries between provinces respond to features of physical oceanography and, ideally, should follow seasonal and interannual changes in ocean dynamics. But this ideal has not been fulfilled except for small regions of the oceans. Moreover, BGCPs have been used only as static entities having boundaries that were originally established to compute global primary production. In the present study, a new statistical methodology based on non-parametric procedures is implemented to capture the environmental characteristics within 56 BGCPs. Four main environmental parameters (bathymetry, chlorophyll a concentration, surface temperature, and salinity) are used to infer the spatial distribution of each BGCP over 1997–2007. The resulting dynamic partition allows us to integrate changes in the distribution of BGCPs at seasonal and interannual timescales, and so introduces the possibility of detecting spatial shifts in environmental conditions

Glacial‐interglacial variations in sediment organic carbon accumulation and benthic foraminiferal assemblages on the Bermuda Rise (ODP Site 1063) during MIS 13 to 10

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/94866/1/palo1807.pd

A moral panic? The problematization of forced marriage in British newspapers

This paper examines the British media’s construction of forced marriage as an urgent social problem in a context where other forms of violence against women are not similarly problematised. A detailed analysis of four British newspapers over a ten-year period demonstrates that media reporting of forced marriage constitutes a moral panic in that it is constructed as a cultural problem that threatens Britain’s social order rather than as a specific form of violence against women. Thus, the current problematisation of forced marriage restricts discursive spaces for policy debates and hinders attempts to respond to this problem as part of broader efforts to tackle violence against women

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages