Olivares Rodrigo, Salinas Omar, Ravelo Camilo, Soto Ricardo, Crawford Broderick
Escuela de Ingeniería Informática, Universidad de Valparaíso, Valparaíso 2362905, Chile.
Escuela de Ingeniería y Negocios, Universidad Viña del Mar, Viña del Mar 2572007, Chile.
Biomimetics (Basel). 2024 May 21;9(6):307. doi: 10.3390/biomimetics9060307.
In the complex and dynamic landscape of cyber threats, organizations require sophisticated strategies for managing Cybersecurity Operations Centers and deploying Security Information and Event Management systems. Our study enhances these strategies by integrating the precision of well-known biomimetic optimization algorithms-namely Particle Swarm Optimization, the Bat Algorithm, the Gray Wolf Optimizer, and the Orca Predator Algorithm-with the adaptability of Deep Q-Learning, a reinforcement learning technique that leverages deep neural networks to teach algorithms optimal actions through trial and error in complex environments. This hybrid methodology targets the efficient allocation and deployment of network intrusion detection sensors while balancing cost-effectiveness with essential network security imperatives. Comprehensive computational tests show that versions enhanced with Deep Q-Learning significantly outperform their native counterparts, especially in complex infrastructures. These results highlight the efficacy of integrating metaheuristics with reinforcement learning to tackle complex optimization challenges, underscoring Deep Q-Learning's potential to boost cybersecurity measures in rapidly evolving threat environments.
在复杂多变的网络威胁环境中，组织需要完善的策略来管理网络安全运营中心并部署安全信息与事件管理系统。我们的研究通过将著名的仿生优化算法（即粒子群优化算法、蝙蝠算法、灰狼优化算法和逆戟鲸捕食者算法）的精确性与深度Q学习的适应性相结合，提升了这些策略。深度Q学习是一种强化学习技术，它利用深度神经网络，通过在复杂环境中反复试验来教会算法最优行动。这种混合方法旨在实现网络入侵检测传感器的高效分配与部署，同时在成本效益与基本网络安全要求之间取得平衡。全面的计算测试表明，采用深度Q学习增强后的版本显著优于其原始版本，尤其是在复杂的基础设施环境中。这些结果凸显了将元启发式算法与强化学习相结合以应对复杂优化挑战的有效性，强调了深度Q学习在快速演变的威胁环境中提升网络安全措施的潜力。
