摘要
基于机器学习的网络安全防护系统,通过基础层、网络层及分析决策层共同构建,在数据采集与处理阶段,系统收集网络流量和日志数据,并进行预处理和特征提取,以供后续的机器学习模型训练使用。通过机器学习模型训练,系统能够学习网络中的正常行为和异常模式,从而实现对安全事件的检测和预测。通过评估系统测试结果,验证了系统的准确性、召回率、精确度和F1值等指标。测试结果表明,基于机器学习的网络安全防护系统在各项指标上表现出优异的性能,能够准确检测和预测网络中的安全事件发生。
The machine learning-based network security protection system is built by combining the foundation layer, network layer, and analysis decision layer. In the data collection and processing stage, the system collects network traffic and log data, performs preprocessing and feature extraction for subsequent machine learning model training. Through machine learning model training, the system is capable of learning normal behaviors and abnormal patterns in the network, enabling the detection and prediction of security events. The system's accuracy, recall, precision, and F1-score are evaluated through the assessment of system test results. The test results demonstrate the outstanding performance of the machine learning-based network security protection system across various metrics, accurately detecting and predicting security events in the network.
作者
徐圻铠
方梁坤
XU Qikai;FANG Liangkun(Zhejiang Sci-Tech University,Hangzhou Zhejiang 310018)
出处
《软件》
2023年第10期152-154,共3页
Software
关键词
机器学习
网络安全
安全系统
machine learning
network security
security system
