基于神经网络的5G通信网络信息安全防护技术研究

Research on Information Security Protection Technology of 5G Communication NetworkBased on Neural Network

随着5G通信网络的快速发展,其高带宽、低延迟和广泛连接性也带来了严峻的信息安全挑战,如网络入侵、恶意软件攻击和异常流量,对传统的安全防护技术造成了较大的压力。文中提出了一种基于神经网络的5G通信网络信息安全防护技术,旨在利用深度学习技术提升5G网络的安全性,通过构建入侵检测、恶意行为识别和异常流量分析三大核心模块,全面保护5G网络的安全。实验结果表明,该技术在检测准确率、误报率和响应时间方面显著优于传统方法,其中检测准确率提高了15.6%,误报率降低了75.5%,响应时间缩短了25%,为5G网络的信息安全防护提供了有效的解决方案。

With the rapid development of 5G communication network,its high bandwidth,low latency and wide connectivity have also brought severe information security challenges,such as network intrusion,malicious software attacks and abnormal traffic,which have caused great pressure on traditional security protection technologies.This paper proposes a 5G communication network information security protection technology based on neural networks,which aims to use deep learning technology to improve the security of 5G network.The experimental results show that the technology is significantly better than traditional methods in terms of detection accuracy,false positive rate and response time.The detection accuracy is increased by 15.6%,the false positive rate is reduced by 75.5%,and the response time is shortened by 25%.This provides an effective solution for information security protection in 5G networks.