One of the main problems of modern research concerns the optimal design of solutions to address cybersecurity problems, with methods that provide the ability to choose an objective function dierent from that of the classic problem of more economical design while allowing the use of constraints on any possible variable during planning, including nancial resources. e number of corresponding solutions concerns the issue of optimal design of security strategies with a simulation that lies in game theory, with players, the defender on one side defending the systemmanaging the available options of strategic solutions, and the attacker, who chooses the way to strike the system, based on some attack scenarios that cannot be easily predicted. e inherent diculty of implementing the proposed solutions lies in the combined explosion of all possible combinations that make up the solution space, the complete examination of which requires a lot of computational time and computing resources, to the point that their use becomes unprotable. is weakness is attributed to evenminor problems, and the possible strategies available to the defender are nite but at the same time numerous. To solve the abovementioned problem, the work proposes a hybrid system that aims to identify the best possible approach in the theoretically optimal solution in a short time and with minimal computing resources. Specically, a heuristic optimization methodology is used with overlapping answers between two contiguous neighborhoods based on the Bloom Filters structure that supports fast listings and searches. is methodology, which is evaluated in optimizing safety strategies in the sports industry, brings about 40% optimization.

OptimizationofCyberTactics inSports StrategiesUsingHybridAI Decision-Making Technologies

One of the main problems of modern research concerns the optimal design of solutions to address cybersecurity problems, with methods that provide the ability to choose an objective function different from that of the classic problem of more economical design while allowing the use of constraints on any possible variable during planning, including financial resources. The number of corresponding solutions concerns the issue of optimal design of security strategies with a simulation that lies in game theory, with players, the defender on one side defending the system managing the available options of strategic solutions, and the attacker, who chooses the way to strike the system, based on some attack scenarios that cannot be easily predicted. The inherent difficulty of implementing the proposed solutions lies in the combined explosion of all possible combinations that make up the solution space, the complete examination of which requires a lot of computational time and computing resources, to the point that their use becomes unprofitable. This weakness is attributed to even minor problems, and the possible strategies available to the defender are finite but at the same time numerous. To solve the abovementioned problem, the work proposes a hybrid system that aims to identify the best possible approach in the theoretically optimal solution in a short time and with minimal computing resources. Specifically, a heuristic optimization methodology is used with overlapping answers between two contiguous neighborhoods based on the Bloom Filters structure that supports fast listings and searches. This methodology, which is evaluated in optimizing safety strategies in the sports industry, brings about 40% optimization.

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Optimization of Cyber Tactics in Sports Strategies Using Hybrid AI Decision-Making Technologies

This paper proposes a probability density function model based on deep learning to analyze the relationships between the number of call arrivals and vehicle speed. Furthermore, a vehicle speed estimation method based on deep learning is proposed to estimate vehicle speed in accordance with the number of call arrivals. A traffic flow estimation method is proposed to estimate traffic flow in accordance with the number of normal location updates. Finally, a traffic density estimation method is proposed to estimate the traffic density in accordance with the estimated vehicle speed and the estimated traffic flow. In experiments, the simulation results showed that the accuracies of estimated vehicle speed and estimated traffic density are 96.36% and 96.45%, respectively.

Zhaoshan Sun

Papers

A Probability-Based Analytical Model Based on Deep Learning for Traffic Information Estimation