Due to the large scale of the Internet of Things, numerous devices, and the increasing threat of network attacks, traditional network security management methods are no longer able to meet the needs. This article aims to propose an artificial intelligence network security management optimization algorithm based on the full IP Internet of Things fusion technology, in order to improve the efficiency and accuracy of network security management. A network security management model in the Internet of Things environment has been established through research on the integration technology of the all IP Internet of Things. On the basis of establishing a network security management model and combining artificial intelligence technology, a reinforcement learning based network security management optimization algorithm is proposed, which learns the optimal action strategy by interacting with the environment. The article applies it to the field of network security management to learn the behavioral patterns of network attacks. By analyzing network traffic data and attack behavior characteristics, automatically identifying and learning the behavior patterns of attackers, thereby improving network security protection capabilities. Through experimental verification, the algorithm proposed in this article has achieved significant optimization effects in network security management. Compared with traditional methods, this algorithm can more accurately identify and prevent network attacks, and improve the efficiency of network security management.

中文翻译：

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基于All IP物联网融合技术的人工智能网络安全管理优化算法研究

由于物联网规模庞大、设备众多、网络攻击威胁日益增大，传统的网络安全管理方法已经不能满足需求。本文旨在提出一种基于全IP物联网融合技术的人工智能网络安全管理优化算法，以提高网络安全管理的效率和准确性。通过对全IP物联网集成技术的研究，建立了物联网环境下的网络安全管理模型。在建立网络安全管理模型的基础上，结合人工智能技术，提出一种基于强化学习的网络安全管理优化算法，通过与环境的交互来学习最优的行动策略。文章将其应用到网络安全管理领域，学习网络攻击的行为模式。通过分析网络流量数据和攻击行为特征，自动识别和学习攻击者的行为模式，从而提高网络安全防护能力。经过实验验证，本文提出的算法在网络安全管理中取得了显着的优化效果。与传统方法相比，该算法能够更加准确地识别和防范网络攻击，提高网络安全管理的效率。