Risk and Business Goal Based Security Requirement and Countermeasure Prioritization

Companies are under pressure to be in control of their assets but at the same time they must operate as efficiently as possible. This means that they aim to implement “good-enough security” but need to be able to justify their security investment plans. Currently companies achieve this by means of checklist-based security assessments, but these methods are a way to achieve consensus without being able to provide justifications of countermeasures in terms of business goals. But such justifications are needed to operate securely and effectively in networked businesses. In this paper, we first compare a Risk-Based Requirements Prioritization method (RiskREP) with some requirements engineering and risk assessment methods based on their requirements elicitation and prioritization properties. RiskREP extends misuse case-based requirements engineering methods with IT architecture-based risk assessment and countermeasure definition and prioritization. Then, we present how RiskREP prioritizes countermeasures by linking business goals to countermeasure specification. Prioritizing countermeasures based on business goals is especially important to provide the stakeholders with structured arguments for choosing a set of countermeasures to implement. We illustrate RiskREP and how it prioritizes the countermeasures it elicits by an application to an action case

Recovering the state sequence of hidden Markov models using mean-field approximations

Inferring the sequence of states from observations is one of the most fundamental problems in Hidden Markov Models. In statistical physics language, this problem is equivalent to computing the marginals of a one-dimensional model with a random external field. While this task can be accomplished through transfer matrix methods, it becomes quickly intractable when the underlying state space is large. This paper develops several low-complexity approximate algorithms to address this inference problem when the state space becomes large. The new algorithms are based on various mean-field approximations of the transfer matrix. Their performances are studied in detail on a simple realistic model for DNA pyrosequencing.Comment: 43 pages, 41 figure

Jeans that fit : weighing the mass of the Milky Way analogues in the ΛCDM universe

The spherical Jeans equation is a widely used tool for dynamical study of gravitating systems in astronomy. Here, we test its efficacy in robustly weighing the mass of Milky Way analogues, given they need not be in equilibrium or even spherical. Utilizing Milky Way stellar haloes simulated in accordance with Λ cold dark matter (ΛCDM) cosmology by Bullock and Johnston and analysing them under the Jeans formalism, we recover the underlying mass distribution of the parent galaxy, within distance r/kpc ∈ [10, 100], with a bias of ∼ 12 per cent and a dispersion of ∼ 14 per cent. Additionally, the mass profiles of triaxial dark matter haloes taken from the surfs simulation, within scaled radius 0.2 < r/rmax < 3, are measured with a bias of ∼ − 2.4 per cent and a dispersion of ∼ 10 per cent. The obtained dispersion is not because of Poisson noise due to small particle numbers as it is twice the later. We interpret the dispersion to be due to the inherent nature of the ΛCDM haloes, for example being aspherical and out-of-equilibrium. Hence, the dispersion obtained for stellar haloes sets a limit of about 12 per cent (after adjusting for random uncertainty) on the accuracy with which the mass profiles of the Milky Way-like galaxies can be reconstructed using the spherical Jeans equation. This limit is independent of the quantity and quality of the observational data. The reason for a non-zero bias is not clear, hence its interpretation is not obvious at this stage.Publisher PDFPeer reviewe

From Stellar Halos to Intracluster Light: the physics of the Intra-Halo Stellar Component in cosmological hydrodynamical simulations

We study the Intra-Halo Stellar Component (IHSC) of Milky Way-mass systems up to galaxy clusters in the Horizon-AGN cosmological hydrodynamical simulation. We identify the IHSC using an improved phase-space galaxy finder algorithm which provides an adaptive, physically motivated and shape-independent definition of this stellar component, that can be applied to halos of arbitrary masses. We explore the IHSC mass fraction-total halo's stellar mass, $f_{M*,IHSC}-M*$, relation and the physical drivers of its scatter. We find that on average the $f_{M*,IHSC}$ increases with $M_{*,tot}$, with the scatter decreasing strongly with mass from 2 dex at $M_{*,tot}\sim10^{11}M_\odot$ to 0.3 dex at group masses. At high masses, $M_{*,tot}>10^{11.5}M_\odot$, $f_{M*,IHSC}$ increases with the number of substructures, and with the mass ratio between the central galaxy and largest satellite, at fixed $M_{*,tot}$. From mid-size groups and systems below $M_{*,tot}<10^{12}M_\odot$, we find that the central galaxy's stellar rotation-to-dispersion velocity ratio, V/{\sigma}, displays the strongest (anti)-correlation with $f_{M*,IHSC}$ at fixed $M_{*,tot}$ of all the galaxy and halo properties explored, transitioning from $f_{M*,IHSC}$<0.1% for high V/{\sigma}, to $f_{M*,IHSC}\sim5$% for low V/{\sigma} galaxies. By studying the $f_{M*,IHSC}$ temporal evolution, we find that, in the former, mergers not always take place, but if they did, they happened early (z>1), while the high $f_{M*,IHSC}$ population displays a much more active merger history. In the case of massive groups and galaxy clusters, $M_{*,tot}>10^{12}M_\odot$, a fraction $f_{M*,IHSC}\sim$10-20% is reached at $z\sim1$ and then they evolve across lines of constant $f_{M*,IHSC}$ modulo some small perturbations. Because of the limited simulation's volume, the latter is only tentative and requires a larger sample of simulated galaxy clusters to confirm.Comment: 21 pages, 17 figures. Submitted to MNRAS. Comments are welcome

Shark: introducing an open source, free and flexible semi-analytic model of galaxy formation

We present a new, open source, free semi-analytic model (SAM) of galaxy formation, Shark, designed to be highly flexible and modular, allowing easy exploration of different physical processes and ways of modelling them. We introduce the philosophy behind Shark and provide an overview of the physical processes included in the model. Shark is written in C++11 and has been parallelized with OpenMP. In the released version (v1.1), we implement several different models for gas cooling, active galactic nuclei, stellar and photo-ionisation feedback, and star formation (SF). We demonstrate the basic performance of Shark using the Planck15 cosmology SURFS simulations, by comparing against a large set of observations, including: the stellar mass function (SMF) and stellar-halo mass relation at z=0-4; the cosmic evolution of the star formation rate density (SFRD), stellar mass, atomic and molecular hydrogen; local gas scaling relations; and structural galaxy properties, finding excellent agreement. Significant improvements over previous SAMs are seen in the mass-size relation for disks/bulges, the gas-stellar mass and stellar mass-metallicity relations. To illustrate the power of Shark in exploring the systematic effects of the galaxy formation modelling, we quantify how the scatter of the SF main sequence and the gas scaling relations changes with the adopted SF law, and the effect of the starbursts H$_2$ depletion timescale on the SFRD and $\Omega_{\rm H_2}$. We compare Shark with other SAMs and the hydrodynamical simulation EAGLE, and find that SAMs have a much higher halo baryon fractions due to large amounts of intra-halo gas, which in the case of EAGLE is in the intergalactic medium.Comment: Final MNRAS version of accepted pape

Exploring Galaxy Formation Models and Cosmologies with Galaxy Clustering

Using N-body simulations and galaxy formation models, we study the galaxy stellar mass correlation and the two-point auto-correlation. The simulations are run with cosmological parameters from the WMAP first, third and seven year results, which mainly differ in the perturbation amplitude of \sigma_{8}. The stellar mass of galaxies are determined using either a semi-analytical galaxy formation model or a simple empirical abundance matching method. Compared to the SDSS DR7 data at z=0 and the DEEP2 results at z=1, we find that the predicted galaxy clusterings from the semi-analytical model are higher than the data at small scales, regardless of the adopted cosmology. Conversely, the abundance matching method predicts good agreement with the data at both z=0 and z=1 for high \sigma_8 cosmologies (WMAP1 & WMAP7), but the predictions from a low \sigma_8 cosmology (WMAP3) are significantly lower than the data at z=0. We find that the excess clustering at small-scales in the semi-analytical model mainly arises from satellites in massive haloes, indicating that either the star formation is too efficient in low-mass haloes or tidal stripping is too inefficient at high redshift. Our results show that galaxy clustering is strongly affected by the models for galaxy formation, thus can be used to constrain the baryonic physics. The weak dependence of galaxy clustering on cosmological parameters makes it difficult to constrain the WMAP1 and WMAP7 cosmologies.Comment: 8 pages, 7 figures. Accepted to MNRA

The detection of sub-solar mass dark matter halos

Dark matter halos of sub-solar mass are the first bound objects to form in cold dark matter theories. In this article, I discuss the present understanding of "microhalos'', their role in structure formation, and the implications of their potential presence, in the interpretation of dark matter experiments.Comment: 18 pages, 7 figures. Invited contribution to NJP Focus Issue on "Dark Matter and Particle Physics