深度学习在网络入侵检测系统中的应用及其效果  

Application and Effect Analysis of Deep Learning in Network Intrusion Detection System

作　　者：刘文 孟林溪 LIU Wen;MENG Linxi(Qingdao City University,Qingdao,Shandong 266106,China)

机构地区：[1]青岛城市学院,山东青岛266106

出　　处：《移动信息》2024年第8期220-222,共3页MOBILE INFORMATION

摘　　要：随着网络技术的快速发展,网络安全问题日益突显,网络入侵检测系统(NIDS)成为保障网络安全的重要工具。然而,传统的NIDS面临着处理高动态网络环境的挑战,难以满足大规模数据处理、准确率及低误报率方面的需求。近年来,深度学习技术因其在数据分析和特征提取方面的优异表现,被视为提升NIDS性能的有力工具。文中通过实验比较,分析了深度学习技术在NIDS中的应用效果,特别是在数据预处理、模型选择与优化、攻击检测准确率等方面的表现。结果表明,深度学习技术能显著提高检测准确率,降低误报率,对于处理大规模网络数据具有明显优势,展现了其在网络入侵检测领域的广泛应用前景。With the rapid development of network technology,cyber security issues have become increasingly prominent,and network intrusion detection system(NIDS)has become an important tool to ensure cyber security.However,traditional NIDS is facing the challenge of dealing with high-dynamic network environments,and it is difficult to meet the needs of large-scale data processing,accuracy and low false alarm rate.In recent years,deep learning technology has been regarded as a powerful tool to improve the performance of NIDS due to its excellent performance in data analytics and feature extraction.Through experimental comparison,this paper analyzes the application effect of deep learning technology in NIDS,especially in data preprocessing,model selection and optimization,attack detection accuracy and so on.The results show that deep learning technology can significantly improve the detection accuracy and reduce the false alarm rate,and has obvious advantages for processing largescale network data,showing its wide application prospects in the field of network intrusion detection.

关 键 词：深度学习 网络入侵检测 应用 效果分析 

分 类 号：TN915.08[电子电信—通信与信息系统]