Cyber security risk management plays an important role for today’s businesses due to the rapidly changing threat landscape and the existence of evolving sophisticated cyber attacks. It is necessary ...for organisations, of any size, but in particular those that are associated with a critical infrastructure, to understand the risks, so that suitable controls can be taken for the overall business continuity and critical service delivery. There are a number of works that aim to develop systematic processes for risk assessment and management. However, the existing works have limited input from threat intelligence properties and evolving attack trends, resulting in limited contextual information related to cyber security risks. This creates a challenge, especially in the context of critical infrastructures, since attacks have evolved from technical to socio-technical and protecting against them requires such contextual information. This research proposes a novel integrated cyber security risk management (i-CSRM) framework that responds to that challenge by supporting systematic identification of critical assets through the use of a decision support mechanism built on fuzzy set theory, by predicting risk types through machine learning techniques, and by assessing the effectiveness of existing controls. The framework is composed of a language, a process, and it is supported by an automated tool. The paper also reports on the evaluation of our work to a real case study of a critical infrastructure. The results reveal that using the fuzzy set theory in assessing assets' criticality, our work supports stakeholders towards an effective risk management by assessing each asset's criticality. Furthermore, the results have demonstrated the machine learning classifiers’ exemplary performance to predict different risk types including denial of service, cyber espionage and crimeware.
In 1965 Erdős asked, what is the largest size of a family of k-element subsets of an n-element set that does not contain a matching of size s+1? In this note, we improve upon a recent result of ...Frankl and resolve this problem for s>101k3 and (s+1)k⩽n<(s+1)(k+1100k).
Defending Wittgenstein Dehnel, Piotr
Philosophical investigations,
January 2024, 2024-01-00, 20240101, Letnik:
47, Številka:
1
Journal Article
Recenzirano
Samuel J. Wheeler defends Wittgenstein's criticism of Cantor's set theory against the objections raised by Hilary Putnam. Putnam claims that Wittgenstein's dismissal of the basic tenets of this set ...theory concerning the noncountability of the set of real numbers was unfounded and ill‐conceived. In Wheeler's view, Putnam's charges result from his failure to grasp Wittgenstein's intention and, in particular, to consider the difference between empirical and logical impossibility. In my paper, I argue that Wheeler's defence is unsuccessful and, at the same time, that Putnam's objections go too far.
In the real world the decisions are frequently made by a group of decision makers. Methods to support group multicriteria decision making (MCDM) in dynamic environments is a challenging research ...topic under investigation. However, in most of those methods, it is necessary that the decision makers reach an agreement in the setup of the problem. For example, it is common that a group MCDM method requires the decision makers to define jointly a set of criteria. This may not be easy or, even, achievable. Also, the MCDM methods have been extensively generalized to process many different types of information, e.g., crisp, interval, fuzzy, intuitionistic fuzzy, hesitant fuzzy. Nevertheless, many group MCDM methods strongly restrict the freedom of the decision makers to use the type of information they see fit by forcing them to prior define the type of information that must be used. These restrictions considerably reduce the individual opinions of the decision makers involved. In this work, we introduce a generalization of the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method, called GMo-RTOPSIS (Group Modular Random TOPSIS), which provides freedom for the decision makers express his/her individuality and opinions. The method is capable of dealing with an imperfect setting where each decision maker can define independently the criteria set, the weight vector, the underlying factors that may affect the alternatives’ ratings and the type of information they want to use in each criterion. We then show the feasibility of the method by discussing three case studies.
Decision Support Systems (DSSs) are solutions that serve decision-makers in their decision-making process. For the development of these intelligent systems, two primary components are needed: the ...knowledge database and the knowledge rule base. The objective of this research work was to implement and validate diverse clinical decision support systems supported by Mamdani-type fuzzy set theory using clustering and dynamic tables. The outcomes were evaluated with other works obtained from the literature to validate the suggested fuzzy systems for categorizing the Wisconsin breast cancer dataset. The fuzzy Inference Systems worked with different input features, according to the studies obtained from the literature. The outcomes confirm that most performance' metrics in several cases were greater than the achieved results from the literature for the output variable for the different Fuzzy Inference Systems-FIS, demonstrating superior precision.
This paper introduces a dynamic risk assessment method for hydrogen leakage at hydrogen stations, employing fuzzy dynamic Bayesian networks. To begin, we utilize the Bow-Tie model for an in-depth ...analysis and consolidation of the primary risk factors contributing to hydrogen leak explosions. Subsequently, we establish a dynamic Bayesian network model for hydrogen leakage at hydrogen stations, adhering to the mapping rules derived from the Bow-Tie model. In order to reduce the impact of subjectivity, our model derives event prior probabilities through expert scoring and fuzzy set theory. Furthermore, the inclusion of a time factor is followed by the application of the Leaky Noisy-or gate model to enhance and calculate conditional probabilities, leading to the generation of time-series change curves for hydrogen leak probability and the probabilities associated with each accident consequence. The research findings yield valuable insights for risk assessment, accident prevention, and emergency management at hydrogen stations.
•Proposed a dynamic risk assessment model for hydrogen station leaks.•Determined prior probabilities based on fuzzy set theory and expert ratings.•Obtained dynamic leakage probabilities for hydrogen stations.•Provided relevant recommendations for the safe operation of hydrogen stations.
•Analyzed the Sustainable Consumption and Production (SCP) adoption drivers.•Selected fourteen drivers to SCP adoption from literature and expert feedback.•Evaluated the drivers’ casual interaction ...using Grey-DEMATEL approach.•Illustrated the proposed model applicability by taking an Indian automotive case example.•Distinguished ten drivers as influencing and four as influenced drivers in the findings.
Sustainable Consumption and Production (SCP) patterns are becoming important in the implementation of sustainability in industrial contexts. In this sense, this study uniquely focuses on developing a structural model to evaluate the sustainable consumption and production adoption drivers and to improve sustainability aspects in the supply chain scenario under uncertain environments. Initially, fourteen drivers related to sustainable consumption and production adoption were selected from the literature and expert feedback. Then, the grey based Decision Making Trial and Evaluation Laboratory technique was used; this approach not only helps to identify the causal relationships between the selected drivers but also helps to evaluate the strength of their interrelationships. The findings indicate that ten drivers are considered influencing drivers and four drivers are called influenced drivers. “Governmental policies and regulations to develop sustainable consumption and production focused system” and “Management support, dedication and involvement in sustainable consumption and production implementation” have been found as the most influencing drivers and “Gaining the market edge and improving the overall performance” and “Initiatives and promotional schemes regulated by various agencies in sustainable consumption and production implementation” the most easily influenced drivers. This work features an Indian automotive case example to show the proposed model applicability. The finding of this work provide a structural support to the managers by knowing the cause (influencing) and effect group (influenced) drivers in sustainable consumption and production implementation in industrial supply chains. By knowing the cause and effect group drivers, managers can more easily analyze the relevant issues in sustainable consumption and production adoption on the shop floor and, consequently, will be better able to improve overall performance. Finally, the unique contributions and limitation of the work are highlighted to provide a foundation for future research.
A system which couples an abstract hemivariational inequality of hyperbolic type and an evolution equation in a Banach space is studied. The global existence of the system is established by ...exploiting the Rothe method. An application to a dynamic adhesive viscoelastic contact problem with friction is provided for which results on existence and regularity of weak solutions are proved.
In this paper, a sampled-data fuzzy controller is designed to stabilize a class of chaotic systems. A Takagi-Sugeno (T-S) fuzzy model is employed to represent the chaotic systems. Based on this ...general model, the exponential stability issue of the closed-loop systems with an input constraint is first investigated by a novel time-dependent Lyapunov functional, which is positive definite at sampling times but not necessary between the sampling times. Then, two sufficient conditions are developed for sampled-data fuzzy controller synthesis of the underlying T-S fuzzy model with or without input constraint. All the proposed results in this paper depend on both the upper and lower bounds on a sampling interval, and the available information about the actual sampling pattern is fully utilized. The proposed sampled-data fuzzy control scheme is successfully applied to the chaotic Lorenz system, which is shown to be effective and less conservative compared with existing results.
•We investigate the problem of green supplier development program evaluation.•The approach can be applied under limited or no quantitative data.•Program evaluation uses linguistic ratings (fuzzy ...numbers) obtained from experts.•An integrated approach based on NGT and fuzzy VIKOR is proposed.•Sensitivity analysis is performed.
Developing environmental performance of suppliers is critical for green supply chain management. Organizations are nowadays investing in various green supplier development programs to enhance their supplier performances. The decision to select the right program for green supplier development is often a challenging decision due to lack of prior experience, limited quantitative information, specific context of the organization, and varying supplier backgrounds. This paper addresses the problem of evaluating green supplier development programs and proposes a fuzzy NGT (Nominal Group Technique)-VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje) based solution approach. NGT is used to identify criteria for evaluating green supplier development programs. Fuzzy theory is used to address qualitative (linguistic) ratings for the alternatives and the selected criteria used under lack of quantitative information. VIKOR is used to generate green supplier development program rankings and recommend the best program(s) for implementation. Sensitivity analysis is performed to determine the influence of modeling parameters on ranking results of alternatives. A numerical application is provided.
