Perspective Tools to Improve Machine Learning Applications for Cyber Security

Perspective Tools to Improve Machine Learning Applications for Cyber Security

Vardan Mkrttchian (HHH University, Australia) and Leyla Ayvarovna Gamidullaeva (Penza State University, Russia)
Copyright: © 2020 |Pages: 10
DOI: 10.4018/978-1-5225-9715-5.ch071

Abstract

This article presents and briefly discusses the possible results of the use of avatar-based management (A-BM) techniques for designing new tools to improve machine learning applications as the technologies for the cybersecurity systems. This article aims to develop possible solutions to deal with the problem of cybersecurity escalation in of the government and private organizations. The research indicated that currently machine learning applications for cybersecurity need new tools with software for network monitoring use of blockchain technology.
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Introduction

Today security systems suffer from low detection rates and high false alarm rates. In order to overcome such challenging problems, there has been a great number of research conducted to apply Machine Learning (ML) algorithms (Tran, et al., 2012).

Machine learning techniques have been successfully applied to several real world problems in areas as diverse as image analysis, Semantic Web, bioinformatics, text processing, natural language processing, telecommunications, finance, medical diagnosis, and so forth (Gama, and Carvalho, 2012). Recent definition of machine learning is developed by I. Cadez, P. Smyth, H. Mannila, A. Salah, E. Alpaydin (Cadez, et al., 2001; Salah and Alpaydin, 2004).

The issues of the use of machine learning in cyber security are disclosed in many works (Anagnostopoulos, 2018; Edgar and Manz, 2017; Yavanoglu and Aydos, 2017;Khan, et al., 2014; Khan, 2019; Dinur, 2018). Using data mining and machine learning methods for cyber security intrusion detection is proposed by the authors (Kumar, et al., 2017). Object classification literature shows that computer software and hardware algorithms are increasingly showing signs of cognition and are necessarily evolving towards cognitive computing machines to meet the challenges of engineering problems (Khan, et al, 2014). For instance, in response to the continual mutating nature of cyber security threats, basic algorithms for intrusion detection are being forced to evolve and develop into autonomous and adaptive agents, in a manner that is emulative of human information processing mechanisms and processes (Khan, et al., 2014; Khan, 2019).

The maintenance of cyber security can significantly differ depending on the requirements for the control system, its purpose, the specificity of the managed object, the environmental conditions, the composition and state of the forces and controls, and the management order. Why do we need to distinguish between information and cyber security? What tasks can be achieved with this distinction?

This need is conditioned by the transition to a new socio-economic formation, called the information society. If earlier the problems of ensuring cyber security were relevant mainly for the military organization, in connection with the existence and development of the forces and means of information confrontation and electronic warfare, now such problems exist for the state as a whole.

Thus, the tasks of ensuring cyber security for today exist, both for the state as a whole, and for certain critical structures, systems and objects (Mkrttchian, et. al, 2019).

Key Terms in this Chapter

Blockchain: Is a growing list of records, called blocks, which are linked using cryptography. Each block contains a cryptographic hash of the previous block a timestamp, and transaction data.

Machine Learning Application: Is class of methods of artificial/natural intelligence, the characteristic feature of which is not a direct solution of the problem but training in the process of applying solutions to a set of similar problems.

Audit and Policy Mechanisms: Is a section of avatar-based management techniques.

Maturity Models: Is a section of avatar-based management.

Predictive Analytics: Is the use of data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on empirical data.

Avatar-Based Management: Is control methods and techniques introduced by V. Mkrttchian in 2018.

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