电子信息工程中基于深度学习的网络安全策略优化

Network Security Strategy Optimization Based on Deep Learning in Electronic Information Engineering

随着网络攻击途径的不断变化,传统的网络安全策略面临越来越多的挑战。针对该问题问题,文中探讨了深度学习技术在优化网络安全策略中的应用,尤其是长短期记忆网络(LSTM)在入侵检测系统中的使用。通过分析现有网络安全挑战和深度学习的优势,基于模拟医院网络环境进行实验验证。结果显示,与传统方法相比,LSTM模型能有效识别多种网络威胁,具有更高的检测率和较低的误报率,验证了深度学习在网络安全领域的应用潜力,也为未来的网络安全策略提供了新的思路。

With the continuous change of network attack routes,traditional cyber security strategies are facing more and more challenges.In response to this problem,this paper discusses the application of deep learning technology in optimizing cyber security strategies,especially the use of long short-term memory network(LSTM)in intrusion detection systems.By analyzing the existing cyber security challenges and the advantages of deep learning,experiments are carried out based on simulated hospital network environments.The results show that compared with traditional methods,the LSTM model can effectively identify multiple network threats,with higher detection rate and lower false positive rate,verifying the application potential of deep learning in the field of cyber security,and also providing new ideas for future cyber security strategies.