Intelligent computer network security defense strategy based on multi objective optimization algorithm

With the continuous advancement of information technology (IT), network attack methods are increasingly showing a trend of diversification and multidirectionality. In this context, artificial intelligence (AI) technology has improved network security protection capabilities by constructing adaptive intrusion detection models, malicious traffic detection and classification algorithms. This article focuses on intelligent computer network security defense strategies. By constructing a security defense model based on multi-objective optimization algorithms and combining deep learning (DL) technology, an innovative network security protection method is proposed. The defense model proposed in this article innovatively integrates convolutional neural network (CNN) and reinforcement learning (RL) techniques. Among them, CNN is responsible for feature extraction and pattern recognition of network attacks, while RL dynamically optimizes security policies by simulating the adversarial process between attacking agents and defending agents. This dual mechanism can not only effectively identify various types of network attacks, but also continuously improve defense capabilities through continuous learning. The results indicate that the model exhibits significant advantages in both attack recognition accuracy and defense efficiency.