How to assess measurement capabilities of a security monitoring infrastructure and plan investment through a graph-based approach

Security monitoring is a crucial activity in managing cybersecurity for any organization, as it plays a foundational role in various security processes and systems, such as risk identification and threat detection. To be effective, security monitoring is currently implemented by orchestrating multiple data sources to provide corrective actions promptly. Poor monitoring management can compromise an organization’s cybersecurity posture and waste resources. This issue is further exacerbated by the fact that monitoring infrastructures are typically managed with a limited resource budget. This paper addresses the problem of supporting security experts in managing security infrastructures efficiently and effectively by considering the trade-off cost-benefit between using specific monitoring tools and the benefit of including them in the organization’s infrastructure. To this aim, we introduce a graph-based model named Metric Graph Model (MGM) to represent dependencies between security metrics and the monitoring infrastructure. It is used to solve a set of security monitoring problems: (i) Metrics Computability, to assess the measurement capabilities of the monitoring infrastructure, (ii) Instrument Redundancy, to assess the utility of the instruments used for the monitoring, and (iii) Cost-Bounded Constraint, to identify the optimal monitoring infrastructure in terms of cost-benefit trade-off. We prove the NP-hardness of some of these problems, propose heuristics for solving them based on the Metric Graph Model and provide an experimental evaluation that shows their better performance than existing solutions. Finally, we present a usage scenario based on an instance of the Metric Graph Model derived from a state-of-the-art security metric taxonomy currently employed by organizations. It demonstrates how the proposed approach supports an administrator in optimizing the security monitoring infrastructure in terms of saving resources and speeding up the decision-making process

Hydrogeological modeling in groundwater engineering structures

The aim of this study is to create an equalization and homogenization tank in an area adjacent to the depuration plant of Verziano (Brescia Province, Italy), using existing unconfined groundwater. This has caused problems in both planning and implementation of this project and has also affected costs. A flow model was elaborated by means of MODFLOW software in the steady-state mode, which considered the various interventions to be undertaken in order to lower the water table (bulkheads, wellpoints and drains) and create the underground tank. The modeled area is 18,492 m2 and was discretized with a dishomogeneous grid consisting of 96 lines and 247 columns. The vertical dimension was sub-divided into 44 layers.A number of simulations were elaborated in an attempt to recreate the real sequence of the interventions undertaken and some significant elements have been. From the simulation results using bulkheads, it was shown that the construction of diaphragms allows the flow of groundwater to be diverted under the panels, according to grain-size distribution. From the wellpoint simulations it was shown that when two microwells are placed too close together, the system becomes inefficient as there is interference between the two cones. The interventions considered cause a variation of groundwater localized only in the area of the tank, without exerting any influence on hydrogeologic features (springs) present in the surrounding area

Use of testosterone replacement therapy to treat long-COVID-related hypogonadism

Summary: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can impair pituitary-gonadal axis and a higher prevalence of hypogonadism in post-coronavirus disease 2019 (COVID-19) patients compared with the general population has been highlighted. Here we report the first case of a patient affected with a long-COVID syndrome leading to hypogonadism and treated with testosterone replacement therapy (TRT) and its effects on clinical and quality of life (QoL) outcomes. We encountered a 62-year-old man who had been diagnosed with hypogonadotropic hypogonadism about 2 months after recovery from COVID-19 underwent a complete physical examination, general and hormonal blood tests, and self-reported questionnaires administration before and after starting TRT. Following the TRT, both serum testosterone level and hypogonadism-related symptoms were improved, but poor effects occurred on general and neuropsychiatric symptoms and QoL. Therefore, hypogonadism does not appear to be the cause of neurocognitive symptoms, but rather a part of the long-COVID syndrome; as a consequence, starting TRT can improve the hypogonadism-related symptoms without clear benefits on general clinical condition and QoL, which are probably related to the long-COVID itself. Longer follow-up might clarify whether post-COVID hypogonadism is a transient condition that can revert as the patient recovers from long-COVID syndrome. Learning points: Hypogonadism is more prevalent in post-COVID-19 patients compared with the general population. In these patients, hypogonadism may be part of long-COVID syndrome, and it is still unclear whether it is a transient condition or a permanent impairment of gonadal function. Testosterone replacement therapy has positive effects on hypogonadism-related clinic without clear benefits on general symptomatology and quality of life, which are more likely related to the long-COVID itself

A MULTI-LAYER ATTACK MODEL INTEGRATING HUMAN FACTORS IN DELIVERING CYBERSECURITY

This study proposes an innovative multi-layer attack model for cybersecurity that integrates human, access, and network layers. In particular, it focuses on the human layer which has been recently recognized as a still open issue. Drawing on literature, human factors (HFs) that contribute to cyber vulnerabilities and human behaviors that can lead to vulnerabilities are identified. Finally, the research discusses human capabilities that could be leveraged as mitigation factors. By considering the HFs from a twofold perspective, the study provides a holistic approach that accounts for both technical and human elements in cybersecurity management

A compliance assessment system for Incident Management process

The Incident Management (IM) process is one of the core activities for increasing the overall security level of organizations and better responding to cyber attacks. Different security frameworks (such as ITIL and ISO 27035) provide guidelines for designing and properly implementing an effective IM process. Currently, assessing the compliance of the actual process implemented by an organization with such frameworks is a complex task. The assessment is mainly manually performed and requires much effort in the analysis and evaluation. In this paper, we first propose a taxonomy of compliance deviations to classify and prioritize the impacts of non-compliant causes. We combine trace alignment techniques with a new proposed cost model for the analysis of process deviations rather than process traces to prioritize interventions. We put these contributions into use in a system that automatically assesses the IM process compliance with a reference process model (e.g., the one described in the chosen security framework). It supports the auditor with increased awareness of process issues to make more focused decisions and improve the process’s effectiveness. We propose a benchmark validation for the model, and we show the system’s capability through a usage scenario based on a publicly available dataset of a real IM log. The source code of all components, including the code used for benchmarking, is publicly available as open source on GitHub

Erratum to: Single‐Photon‐Emission Computed Tomography (SPECT) with Technetium‐99m Sestamibi in the Diagnosis of Small Breast Cancer and Axillary Lymph Node Involvement

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Adaptive free energy sampling in multidimensional collective variable space using boxed molecular dynamics

The past decade has seen the development of a new class of rare event methods in which molecular configuration space is divided into a set of boundaries/interfaces, and then short trajectories are run between boundaries. For all these methods, an important concern is how to generate boundaries. In this paper, we outline an algorithm for adaptively generating boundaries along a free energy surface in multi-dimensional collective variable (CV) space, building on the boxed molecular dynamics (BXD) rare event algorithm. BXD is a simple technique for accelerating the simulation of rare events and free energy sampling which has proven useful for calculating kinetics and free energy profiles in reactive and non-reactive molecular dynamics (MD) simulations across a range of systems, in both NVT and NVE ensembles. Two key developments outlined in this paper make it possible to automate BXD, and to adaptively map free energy and kinetics in complex systems. First, we have generalized BXD to multidimensional CV space. Using strategies from rigid-body dynamics, we have derived a simple and general velocity-reflection procedure that conserves energy for arbitrary collective variable definitions in multiple dimensions, and show that it is straightforward to apply BXD to sampling in multidimensional CV space so long as the Cartesian gradients ∇CV are available. Second, we have modified BXD to undertake on-the-fly statistical analysis during a trajectory, harnessing the information content latent in the dynamics to automatically determine boundary locations. Such automation not only makes BXD considerably easier to use; it also guarantees optimal boundaries, speeding up convergence. We have tested the multidimensional adaptive BXD procedure by calculating the potential of mean force for a chemical reaction recently investigated using both experimental and computational approaches - i.e., F + CD3CN → DF + D2CN in both the gas phase and a strongly coupled explicit CD3CN solvent. The results obtained using multidimensional adaptive BXD agree well with previously published experimental and computational results, providing good evidence for its reliability

Deriving Natural Background Levels of Arsenic at the Meso-Scale Using Site-Specific Datasets: An Unorthodox Method

Arsenic is found in groundwater above regulatory limits in many countries and its origin is often from natural sources, making the definition of Natural Background Levels (NBLs) crucial. NBL is commonly assessed based on either dedicated small-scale monitoring campaigns or large-scale national/regional groundwater monitoring networks that may not grab local-scale heterogeneities. An alternative method is represented by site-specific monitoring networks in contaminated/polluted sites under remediation. As a main drawback, groundwater quality at these sites is affected by human activities. This paper explores the potential for groundwater data from an assemblage of site-specific datasets of contaminated/polluted sites to define NBLs of arsenic (As) at the meso-scale (order of 1000 km2). Common procedures for the assessment of human influence cannot be applied to this type of dataset due to limited data homogeneity. Thus, an \u201cunorthodox\u201d method is applied involving the definition of a consistent working dataset followed by a statistical identification and critical analysis of the outliers. The study was conducted in a highly anthropized area (Ferrara, N Italy), where As concentrations often exceed national threshold limits in a shallow aquifer. The results show that site-specific datasets, if properly pre-treated, are an effective alternative for the derivation of NBLs when regional monitoring networks fail to catch local-scale variability

Wiskott-Aldrich syndrome protein-mediated actin dynamics control type-I interferon production in plasmacytoid dendritic cells

Mutations in Wiskott-Aldrich syndrome (WAS) protein (WASp), a regulator of actin dynamics in hematopoietic cells, cause WAS, an X-linked primary immunodeficiency characterized by recurrent infections and a marked predisposition to develop autoimmune disorders. The mechanisms that link actin alterations to the autoimmune phenotype are still poorly understood. We show that chronic activation of plasmacytoid dendritic cells (pDCs) and elevated type-I interferon (IFN) levels play a role in WAS autoimmunity. WAS patients display increased expression of type-I IFN genes and their inducible targets, alteration in pD

The construction of viewpoint aspect: the imperfective revisited

This paper argues for a constructionist approach to viewpoint Aspect by exploring the idea that it does not exert any altering force on the situation-aspect properties of predicates. The proposal is developed by analyzing the syntax and semantics of the imperfective, which has been attributed a coercer role in the literature as a de-telicizer and de-stativizer in the progressive, and as a de-eventivizer in the so-called ability (or attitudinal) and habitual readings. This paper proposes a unified semantics for the imperfective, preserving the properties of eventualities throughout the derivation. The paper argues that the semantics of viewpoint aspect is encoded in a series of functional heads containing interval-ordering predicates and quantifiers. This richer structure allows us to account for a greater amount of phenomena, such as the perfective nature of the individual instantiations of the event within a habitual construction or the nonculminating reading of perfective accomplishments in Spanish. This paper hypothesizes that nonculminating accomplishments have an underlying structure corresponding to the perfective progressive. As a consequence, the progressive becomes disentangled from imperfectivity and is given a novel analysis. The proposed syntax is argued to have a corresponding explicit morphology in languages such as Spanish and a nondifferentiating one in languages such as English; however, the syntax-semantics underlying both of these languages is argued to be the same