Eloy Vicente

A New Similarity Function for Generalized Trapezoidal Fuzzy Numbers

Numerous authors have proposed functions to quantify the degree of similarity between two fuzzy numbers using various descriptive parameters, such as the geometric distance, the distance between the centers of gravity or the perimeter. However, these similarity functions have drawbacks for specific situations. We propose a new similarity measure for generalized trapezoidal fuzzy numbers aimed at overcoming such drawbacks. This new measure accounts for the distance between the centers of gravity and the geometric distance but also incorporates a new term based on the shared area between the fuzzy numbers. The proposed measure is compared against other measures in the literature.

Similarity functions for generalized trapezoidal fuzzy numbers: An improved comparative analysis

Different functions for quantifying the degree of similarity between two fuzzy numbers have been proposed in the literature on the basis of various descriptive parameters, such as the geometric distance, the distance between the centers of gravity, the perimeters or the shared area between the fuzzy numbers. However, these similarity functions are not equally applicable across all situations. Consequently, comparative analyses have been performed on the basis of different sets of fuzzy number pairs. In this paper we thoroughly review the similarity functions proposed in the literature, identify their properties and possible drawbacks and compare them on the basis of an extended set of fuzzy numbers.

A Fuzzy Approach based on Dynamic Programming and Metaheuristics for Selecting Safeguards for Risk Management for Information Systems

In this paper we focus on the selection of safeguards in a fuzzy risk analysis and management methodology for information systems (IS). Assets are connected by dependency relationships, and a failure of one asset may affect other assets. After computing impact and risk indicators associated with previously identified threats, we identify and apply safeguards to reduce risks in the IS by minimizing the transmission probabilities of failures throughout the asset network. However, as safeguards have associated costs, the aim is to select the safeguards that minimize costs while keeping the risk within acceptable levels. To do this, we propose a dynamic programming-based method that incorporates simulated annealing to tackle optimizations problems.

Risk analysis in information systems: a fuzzification of the MAGERIT methodology

Several methodologies based on ISO/IEC 27000 international standard have been developed to deal with risk analysis in information systems (IS). These methodologies do not, however, consider imprecise valuations, but use precise values on different, usually percentage, scales. We propose an extension of the MAGERIT methodology based on classical fuzzy computational models. A linguistic term scale is used to represent asset values, their dependencies and frequency and asset degradation associated with threats. Computations are based on trapezoidal fuzzy numbers associated with linguistic terms. A similarity function is used to associate a linguistic term on the previously defined scale to the trapezoidal fuzzy numbers resulting from computations. Finally, regarding the selection of preventive safeguards to reduce risks in IS, we propose a dynamic programming-based method that incorporates simulated annealing to tackle optimizations problems with the aim of minimizing costs while keeping the risk at acceptable levels. An example of an administrative unit using in-house and third-party information systems internally and to provide public information services is used to illustrate the methodology.

Complicity Functions for Detecting Organized Crime Rings

Graph theory is an evident paradigm for analyzing social networks, which are the main tool for collective behavior research, addressing the interrelations between members of a more or less well-defined community. Particularly, social network analysis has important implications in the fight against organized crime, business associations with fraudulent purposes or terrorism.

Selection of Safeguards for Fuzzified Risk Management in Information Systems

This paper deals with the selection of failure transmission, preventive and palliative safeguards that minimize the maximum risk caused by threats to the assets of an information system (IS) for a given budget. We assume that all the elements in the IS, i.e., the degree of dependence between assets, the valuations of the assets, the severity and frequency of the threats, and the effect induced by safeguards, can be valuated using a fuzzy linguistic scale. This is less stressful on experts and suitable for accounting for imprecision and/or vagueness about the inputs. We model and solve the respective fuzzy optimization problem by means of the simulated annealing metaheuristic and give an example to illustrate the safeguard selection process.

A Fuzzy Model for Selecting Safeguards to Reduce Risks in Information Systems

Information systems can be represented by acyclic directed graphs where the nodes denote assets and the arcs connecting nodes represent the degree of dependency between assets. Threats are events that can trigger an incident in the organization, causing damage or intangible material loss to assets, and safeguards are measures for addressing threats. In this paper, we propose a fuzzy approach for selecting safeguards that minimizes costs while keeping the degree of dependency between support assets and terminal assets within acceptable levels. The approach is based on dynamic programming and uses the simulated annealing metaheuristic to solve optimization problems.