Enhancing the Efficiency of a Cybersecurity Operations Center Using Biomimetic Algorithms Empowered by Deep...

Area of Science:

• Cybersecurity Operations
• Artificial Intelligence
• Optimization Algorithms

Background:

• Organizations face complex cyber threats requiring advanced strategies for Cybersecurity Operations Centers and Security Information and Event Management systems.
• Effective deployment of network intrusion detection sensors is crucial for maintaining network security while managing costs.

Purpose of the Study:

• To enhance cybersecurity strategies by integrating biomimetic optimization algorithms with Deep Q-Learning.
• To optimize the allocation and deployment of network intrusion detection sensors for improved cost-effectiveness and security.

Main Methods:

• Integration of Particle Swarm Optimization, Bat Algorithm, Gray Wolf Optimizer, and Orca Predator Algorithm with Deep Q-Learning.
• Utilizing Deep Q-Learning, a reinforcement learning technique, to train algorithms for optimal decision-making in complex environments.
• Conducting comprehensive computational tests to evaluate the performance of the hybrid methodology.

Main Results:

• Hybrid models enhanced with Deep Q-Learning demonstrated superior performance compared to their native biomimetic counterparts.
• Significant improvements were observed in complex network infrastructures.
• The study validated the efficacy of integrating metaheuristics with reinforcement learning for complex optimization challenges.

Conclusions:

• The integration of biomimetic algorithms and Deep Q-Learning offers a powerful approach to enhance cybersecurity operations.
• Deep Q-Learning significantly boosts the effectiveness of cybersecurity measures in dynamic threat landscapes.
• This hybrid methodology provides a robust solution for efficient sensor deployment and network security optimization.