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Machine Learning Application With Avatar-Based Management Security to Reduce Cyber Threat

Machine Learning Application With Avatar-Based Management Security to Reduce Cyber Threat

Vardan Mkrttchian, Leyla Gamidullaeva, Yulia Vertakova, Svetlana Panasenko
Copyright: © 2019 |Pages: 16
ISBN13: 9781522581000|ISBN10: 1522581006|ISBN13 Softcover: 9781522594765|EISBN13: 9781522581017
DOI: 10.4018/978-1-5225-8100-0.ch005
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MLA

Mkrttchian, Vardan, et al. "Machine Learning Application With Avatar-Based Management Security to Reduce Cyber Threat." Machine Learning and Cognitive Science Applications in Cyber Security, edited by Muhammad Salman Khan, IGI Global, 2019, pp. 123-138. https://doi.org/10.4018/978-1-5225-8100-0.ch005

APA

Mkrttchian, V., Gamidullaeva, L., Vertakova, Y., & Panasenko, S. (2019). Machine Learning Application With Avatar-Based Management Security to Reduce Cyber Threat. In M. Khan (Ed.), Machine Learning and Cognitive Science Applications in Cyber Security (pp. 123-138). IGI Global. https://doi.org/10.4018/978-1-5225-8100-0.ch005

Chicago

Mkrttchian, Vardan, et al. "Machine Learning Application With Avatar-Based Management Security to Reduce Cyber Threat." In Machine Learning and Cognitive Science Applications in Cyber Security, edited by Muhammad Salman Khan, 123-138. Hershey, PA: IGI Global, 2019. https://doi.org/10.4018/978-1-5225-8100-0.ch005

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Abstract

This chapter is devoted to studying the opportunities of machine learning with avatar-based management techniques aimed at optimizing threat for cyber security professionals. The authors of the chapter developed a triangular scheme of machine learning, which included at each vertex one participant: a trainee, training, and an expert. To realize the goal set by the authors, an intelligent agent is included in the triangular scheme. The authors developed the innovation tools using intelligent visualization techniques for big data analytic with avatar-based management in sliding mode introduced by V. Mkrttchian in his books and chapters published by IGI Global in 2017-18. The developed algorithm, in contrast to the well-known, uses a three-loop feedback system that regulates the current state of the program depending on the user's actions, virtual state, and the status of implementation of available hardware resources. The algorithm of automatic situational selection of interactive software component configuration in virtual machine learning environment in intelligent-analytic platforms was developed.

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