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

Area of Science:

• Cybersecurity
• Operations Research
• Computer Science

Background:

• Designing optimal cybersecurity solutions faces challenges with complex objective functions and resource constraints.
• Game theory simulations involving defenders and attackers highlight the difficulty in predicting attack scenarios and managing numerous defense strategies.
• The computational complexity of exploring all possible solutions in cybersecurity strategy design is often prohibitive.

Purpose of the Study:

• To develop a hybrid system for the optimal design of cybersecurity strategies.
• To address the computational limitations of traditional methods by reducing time and resource requirements.
• To identify theoretically optimal solutions efficiently.

Main Methods:

• A hybrid system combining heuristic optimization with Bloom Filters is proposed.
• The methodology utilizes overlapping answers between contiguous neighborhoods for fast searching.
• The approach focuses on optimizing security strategies, demonstrated within the sports industry context.

Main Results:

• The proposed hybrid system significantly reduces the time and computational resources needed for cybersecurity strategy design.
• A notable optimization of approximately 40% was achieved in the evaluated application.
• The Bloom Filters structure facilitates efficient listings and searches within the solution space.

Conclusions:

• The hybrid heuristic optimization methodology offers an effective solution for the optimal design of cybersecurity strategies.
• This approach overcomes the computational intractability of exhaustive search methods.
• The system demonstrates practical applicability and efficiency, particularly in complex security environments.