A Security System for National Network

Park, Woo Young; Kim, Sang Hyun; Vu, Duy-Son; Song, Chang Han; Jung, Heesoo; Jo, Hyeon

doi:10.1007/978-3-031-10467-1_48

Cited by 2 publications

(1 citation statement)

References 15 publications

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“…Aspects of the fight against complex cybercrimes are discussed in articles [13,14]. Issues of using neural networks in research related to national security are discussed in articles [15,16]. Aspects of the use of cloud technologies related to this problem are discussed in articles [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Practical Principles of Integrating Artificial Intelligence Into the Technology of Regional Security Predicting

Shefer,

Laktionov,

Pents

et al. 2024

A.I.S.

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Objective. The aim is to enhance the efficiency of diagnostics for determining the level of air attack safety through the practical integration principles of artificial intelligence. Methodology. Models and technologies for safety diagnostics of the region (territorial community) have been explored. The process of building an artificial intelligence model requires differentiation of objects at a level to accumulate assessments-characteristics of aerial vehicles. The practical integration principles of artificial intelligence into the forecasting technology are based on the Region Safety Index, used for constructing machine learning models. The optimal machine learning model of the proposed approach is selected from a list of several models. Results. A technology for predicting the level of regional safety based on the Safety Index has been developed. The recommended optimal model is the Random Forest model ([('max_depth', 13), ('max_features', 'sqrt'), ('min_samples_leaf', 1), ('min_samples_split', 2), ('n_estimators', 79)]), demonstrating the most effective quality indicators of MAE; MAX; RMSE 0.005; 0.083; 0.0139, respectively. Scientific Novelty. The proposed approach is based on a linear model of the Region Safety Index, which, unlike existing ones, takes into account the interaction of factors. This allows for advantages of the proposed method over existing approaches in terms of the root mean square error of 0.496; 0.625, respectively. In turn, this influences the quality of machine learning models. Practical Significance. The proposed solutions are valuable for diagnosing the level of safety in the region of Ukraine, particularly in the context of air attacks.

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