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Evolving fuzzy neural networks to aid in the construction of systems specialists in cyber attacks

机译:不断发展的模糊神经网络,以帮助建设网络攻击中的系统专家

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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.
机译:流程和服务的计算机化的增长改变了人际关系,因此,为数字设备的用户创造了新的形式的攻击和欺诈。由于许多人使用计算机,智能手机和电子邮件来执行日常任务,因此各种数据流量易受攻击。这可以破坏可能违反战略信息的公司的竞争力。因此,安全和信息管理是公司保持适当控制和管理其业务知识的基础因素。网络攻击是由于越来越多的全球保密违约规模,被称为当代世界的重大挑战之一。本文旨在提出基于智能混合模型的计算系统,通过模糊规则允许构建对多种自然的控制网络资料数据的攻击。该测试是对政府计算机化设备数据库的真实攻击基础进行了。本文提出的模型使用模糊不断发展的数据分组概念。极端学习机器执行培训,UNINEURON类型的逻辑神经元负责创建模糊规则,该规则能够将模型所获取的知识转化为公司的员工培训,其他计算机系统的构建和可能的要素的认识损害个人和公司数据的完整性。本文提出的智能技术的新颖性是网络攻击的性质定义了模型的结构,因为模糊和正规化的技术完全基于控制论侵犯的复杂性。与文献的传统模型面对的二进制模式分类测试证明,本文的提议可以维持网络攻击的检测准确性,仍然管理构建一系列规则,该规则作为希望保护其信息的公司的知识来自攻击设备。

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