Abstract
This article is devoted to the problem of identifying technical channels of information leakage using a decision support subsystem and a neural network. Research is aimed at identifying technical channels of information leakage. The article describes a model for ensuring information security, which is based on an adaptive neuro-fuzzy system and a decision support subsystem that ensures the timely identification of technical channels of information leakage, the system also has the functionality to calculate sound insulation, vibration isolation, line-of-sight range, and acoustoelectric conversion. The system specified in the article makes it possible to identify technical channels of information leakage using the description of the premises, and also generates a report on the results of the data obtained with the ability to send such reports via e-mail to a specified address. The practical application of the proposed system is the possibility of its use in the initial research of the protected premises, which allows to build an effective protection system in the organization.
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References
Clark L, Algaier W (2007) Surveillance detection the art of prevention, p 197. Cradle Press LLC
Keen P (1987) Decision support systems: the next decades. Decis Support Syst 3:253–265
Little J (1970) Models and managers: the concept of a decision calculus. Manage Sci 16(8):466–485
Pengyu L, Avraham A (2020) Extracting boolean and probabilistic rules from trained neural networks. Neural Netw 126:300–311
Yong G, Jian C, Qing D, Anton V, Den H, Qinfeng S, Mingkui T (2020) Multi-way backpropagation for training compact deep neural networks. Neural Netw 126:250–261
Zhengxin C (1992) A conceptual framework for expert system description. Comput Edu 18:259–266
Yee L (2020) Artificial intelligence and expert systems. International encyclopedia of human geography, 2nd edn, pp 209–215
Kushal A, Arun M (2018) Theoretical framework of quantitative analysis based information leakage warning system. Karbala Int J Mod Sci 4:151–163
Tao Z, Yangyang S, Yaqin W, Yanzhang G, Long F (2020) An overview of speech endpoint detection algorithms. Appl Acoust 160:107–133
Yu F, Tai A (1975) Information leakage through a passive optical channel. Opt Commun 14:51–55
Khuat T, Gabrys B (2020) Accelerated learning algorithms of general fuzzy min-max neural network using a novel hyperbox selection rule. Inf Sci
Vugar E (2020) Value of fuzzy logic for data mining and machine learning: a case study. Expert Syst Appl 162
Mordechai G, Ofer H, Gabi K, Yuval E (2016) An optical covert-channel to leak data through an air-gap. In: 14th Annual Conference on Privacy, Security and Trust (PST)
Zhentao G, Yuanyuan C, Zhang Y (2020) A novel method to compute the weights of neural networks. Neurocomputing 407:409–427
Mourad G (2020) Hybrid deep neural networks for recommender systems. Neurocomputing 413:23–30
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Razumov, P.V. et al. (2022). Development of an Adaptive Fuzzy Algorithm for Identifying Technical Channels of Information Leakage. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Proceedings of Sixth International Congress on Information and Communication Technology. Lecture Notes in Networks and Systems, vol 216. Springer, Singapore. https://doi.org/10.1007/978-981-16-1781-2_28
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DOI: https://doi.org/10.1007/978-981-16-1781-2_28
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