METHOD OF COMPUTER SYSTEM STATE IDENTIFICATION BASED ON BOOSTING ENSEMBLE WITH SPECIAL PREPROCESSING PROCEDURE
Article Sidebar
Main Article Content
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.
Article Details
References
(2020), “Inter American Development Bank and Organization of American States”, Cybersecurity Risks, Progress, and the Way Forward in Latin America and the Caribbean, pp. 19-38, doi: http://dx.doi.org/10.18235/0002513.
Kumkar, P., Madan, I., Kale, A., Khanvilkar, O. and Khan ,A. (2018), “Comparison of Ensemble Methods for Real Estate Appraisal”, 3rd International Conference on Inventive Computation Technologies (ICICT), Coimbatore, India, pp. 297-300, doi: http://dx.doi.org/10.1109/ICICT43934.2018.9034449.
Lee, S., Son, C., Albertini, M. K. and Fernandes, H. C. (2020), “Multi-Phases and Various Feature Extraction and Selection Methodology for Ensemble Gradient Boosting in Estimating Respiratory Rate”, IEEE Access, Vol. 8, pp. 125648-125658, doi: http://dx.doi.org/10.1109/ACCESS.2020.3007524.
Dutta, J., Kim, Y. W. and Dominic, D. (2020), “Comparison of Gradient Boosting and Extreme Boosting Ensemble Methods for Webpage Classification”, Fifth International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN), Bangalore, India, pp. 77-82. doi: http://dx.doi.org/10.1109/ICRCICN50933.2020.9296176.
Li, Z. and Wang, D. (2020), “Classification on Point-cloud of Shoe-last Curvature using Weight-updated Boosting based Ensemble Learning”, IEEE 9th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), Chongqing, China, , pp. 2073-2077, doi: http://dx.doi.org/10.1109/ITAIC49862.2020.9338947.
Mirza, H. (2018), “Online boosting algorithm for regression with additive and multiplicative updates”, 26th Signal Processing and Communications Applications Conference (SIU), Izmir, pp. 1-4, doi: http://dx.doi.org/10.1109/SIU.2018.8404455.
Chelak, V. and Gavrylenko, S. (2021), “Development of Computer State Identification Method Based on Boosting Ensemble”, Fifth International Scientific and Technical Conference "Computer and Information Systems And Technologies", Kharkiv, рр.53-54, doi: http://dx.doi.org/10.30837/csitic52021232208.