基于机器学习的网络安全攻击防御模型设计

打开文本图片集

中图分类号：TP18 文献标志码：A 文章编号：2095-2945（2025）34-0045-04

Abstract：InviewofthecurrentsituationofDDoSatacksandnetworkattack methodssuchasSQLinjectionandcross-site scripting，anetworksecuritydefense modelbasedondeep leaming andintegratedlearning isdesigned.Thismodelusesa CNNLSTMhybridarchitecturetoextractnetworktraffccharacteristics，andcombinesXGBoostandLightGBMtoachievemulti-level detection.Testsonstandard datasetssuchas CICIDS2019andNSL-KDDshow thatthe modelachieves a detectionaccuracyof （204 98.6% forknown attacks，a recognition rate of 92.3% for unknown attcks，and an average detection latency of less than 100 ms. AfterthreemonthsofdeploymentintheproductionenvironmentofanInteretcompany，maliciousatacktraficwassuccesfly intercepted 850，OOO times，with a false alarm rate ofonly 0.01% ，which confirmed the feasibility and effectiveness of the model in practical applications.

Keywords：deeplearning;network intrusiondetection;multi-layerdefense;CNN-LSTM;integratedlearning;real-time detection

企业级网络环境面临的安全威胁日趋复杂，传统的基于规则匹配和特征库的防御系统难以应对零日漏洞和变种攻击。（剩余5559字）