Research on Cybersecurity Evaluation Algorithms for Computer Networks Based on Deep Learning

Abstract

With the deepening development of the digital age, computer networks have become the core infrastructure for information exchange in modern society. However, they also face numerous security threats, such as data leaks and cyberattacks. Traditional network security protection measures are ineffective against complex attacks, and more intelligent solutions are urgently needed. Deep learning technology, with its superior feature learning capabilities, can automatically identify abnormal patterns in network traffic, improving the detection and defense capabilities against network threats. This paper focuses on the application of deep learning in computer network security assessment and constructs a deep learning network security assessment algorithm. Through a process consisting of data collection and processing, feature extraction, and model training, this algorithm accurately identifies network attack behaviors under real-time monitoring. Experimental results show that compared with traditional methods, the deep learning-based security assessment algorithm achieves significant improvements in accuracy, recall, and F1 score, demonstrating strong application value and promising prospects. By learning from historical data, the algorithm also provides proactive defense strategies for network security, providing technical support for protecting network security.