METHOD OF COMPUTER SYSTEM STATE IDENTIFICATION BASED ON BOOSTING ENSEMBLE WITH SPECIAL PREPROCESSING PROCEDURE

V. Chelak, S. Gavrylenko
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引用次数: 1

Abstract

The subject of the research is methods of identifying the state of the Computer System. The object of research is the process of identifying the state of a computer system for information protection. The aim of the research is to develop the method for identifying the state of a computer system for information protection. This article is devoted to the development of method (boosting ensemble) to increase the accuracy of detecting anomalies in computer systems. Methods used: artificial intelligence methods, machine learning, decision tree methods, ensemble methods. The results were obtained: a method of computer system identification based on boosting ensemble with special preprocessing procedure is developed. The effectiveness of using machine learning technology to identify the state of a computer system has been studied. Experimental researches have confirmed the effectiveness of the proposed method, which makes it possible to recommend it for practical use in order to improve the accuracy of identifying the state of the computer system. Conclusions. According to the results of the research, ensemble classifier of computer system state identification based on boosting was proposed. It was found that the use of the proposed classifier makes it possible to reduce the variance to 10%. In addition, due to the optimization of the initial data, the efficiency of identifying the state of the computer was increased. Prospects for further research may be to develop an ensemble of fuzzy decision trees based on the proposed method, optimizing their software implementation.
基于BOOSTING集成的计算机系统状态识别方法
研究的主题是识别计算机系统状态的方法。研究对象是识别信息保护计算机系统状态的过程。本研究的目的是开发识别信息保护计算机系统状态的方法。本文致力于开发一种提高计算机系统异常检测精度的方法(增强集成)。使用的方法:人工智能方法、机器学习、决策树方法、集成方法。研究结果表明:提出了一种基于boosting集成的计算机系统辨识方法。研究了使用机器学习技术来识别计算机系统状态的有效性。实验研究证实了该方法的有效性,为提高计算机系统状态识别的准确性提供了实用建议。结论。根据研究结果,提出了基于boosting的计算机系统状态识别集成分类器。研究发现,使用所提出的分类器可以将方差降低到10%。此外,由于对初始数据进行了优化,提高了识别计算机状态的效率。进一步研究的前景可能是基于所提出的方法开发一个模糊决策树集合,优化其软件实现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
0.50
自引率
0.00%
发文量
47
审稿时长
5 weeks
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