Machine Learning With Avatar-Based Management of Sleptsov Net-Processor Platform to Improve Cyber Security

Background

Many researchers compare machine learning solutions for cyber security by considering one specific application (e.g., Buczak and Guven, 2016; Blanzieri and Bryl, 2008; Gardiner and Nagaraja, 2016) and are typically oriented to Artificial Intelligence experts.

The term “cyber security” refers to three things:

• 1.

  A set of activities and other measures, technical and non-technical, intended to protect computers, computer networks, related hardware devices and software, and the information they contain and communicate, including software and data, as well as other elements of cyberspace, from all threats, including threats to national security;

• 2.

  The degree of protection resulting from the application of these activities and measures;

• 3.

  The associated field of professional endeavor, including research and analysis, aimed at implementing those activities and improving their quality (Jenab, et al., 2018).

At the same time, our previous research of the problem of cyber security showed that cyber security is a section of information security, within the framework of which the processes of formation, functioning and evolution of cyber objects are studied. It is necessary to identify sources of cyber-danger formed while determining their characteristics, as well as their classification and formation of regulatory documents, implementation of security systems in future. However, working on the application of the machine learning for Cyber Security applications with the use of developed by the authors Avatars-Based Management techniques, we came to the conclusion that this is not so, and the built-in cyber security systems can be destroyed by the same artificial intelligence.

The search for a solution to this discrepancy leads to a thought about advantages of natural intelligence displayed by humans where everything is interconnected, logical and protected (Mkrttchian, et al., 2015).

This paper is specifically aims to research the emerging trends and perspectives of digital transformation of the economy using machine learning with avatar-based management at the platform of Sleptsov net processor, and to identify their main limitations.