Affiliations: [a]
Federal Center for Technological Education of Minas Gerais, CEFET-MG, Av. Amazonas, 5.253, Nova Suica, Belo Horizonte. zip code 30.421-169, Minas Gerais, Brazil
| [b] Una Faculty, Av. Gov. Valadares, 640 - Center, Betim - MG, zip code: 32510-010, Brazil
Correspondence:
[*]
Corresponding author. Paulo Vitor de Campos Souza, E-mail: [email protected].
Abstract: The growth of the computerization of processes and services has changed human relations and, as a consequence, have created new forms of attacks and frauds for users of digital equipment. Because many people use computers, smartphones, and e-mail to perform day-to-day tasks, various data traffic is susceptible to attack. This can undermine the competitiveness of a company that may have breached strategic information. Therefore, security and information management are fundamental factors for companies to keep due control and management of their business knowledge. Cyber attacks are represented by a growing worldwide scale of secrecy breach of relevant information and are characterized as one of the significant challenges of the contemporary world. This article aims to propose a computational system based on intelligent hybrid models, which through fuzzy rules allows the construction of expert systems in attacks on cybernetic data of diverse natures. The tests were carried out with real bases of attacks on the database of governmental computerized devices. The model proposed in this paper uses fuzzy evolving data grouping concepts. The extreme learning machine performs the training and the logical neurons of the unineuron type are responsible for creating fuzzy rules capable of transforming the knowledge acquired by the model into a database for employee training in companies, construction of other computer systems and awareness of elements which may harm the integrity of the data of individuals and companies. The novelty of the intelligent technique presented in the paper is that the nature of cyber attacks defines the structure of the model because the techniques of fuzzification and regularization are based entirely on the complexity of the cybernetic invasions. The binary pattern classification tests confronted with traditional models of the literature prove that the proposal of this paper can maintain the accuracy of detection of cyber attacks and still manages to construct a set of rules that serve as knowledge for the companies that wish to protect their information from attacks devices.
