基于SVM的Web应用异常检测系统的设计与实现

Design and Implementation of Web Application Anomaly Detection System Based on SVM

随着Web应用的普及,网络安全挑战日益增多,异常检测成为保障网络安全的关键技术。基于支持向量机(SVM)的异常检测方法,因其优异的分类性能和泛化能力,具有独特的优势。本文通过深入分析SVM的原理和应用,描述了基于SVM的Web应用异常检测系统的设计过程。利用开源机器学习库sklearn,实现了SVM模型的训练与评估,采用向量化、数据标准化和模型调优等策略,显著提高了异常检测的准确性。实验结果证明,该系统能有效识别Web应用的异常行为,显著增强Web应用的安全。

With the popularization of web applications,network security challenges are increasing day by day,and anomaly detection has become a key technology to ensure network security.The anomaly detection method based on Support Vector Machine(SVM)has unique advantages due to its excellent classification performance and generalization ability.Through in-depth analysis of the principles and applications of SVM,the design process of a web application anomaly detection system based on SVM is described.By utilizing the open-source machine learning library sklearn,the training and evaluation of SVM models were achieved,and strategies such as vectorization,data standardization,and model tuning were adopted,significantly improving the accuracy of anomaly detection.The experimental results prove that the system can effectively identify abnormal behavior of web applications and significantly enhance the security of web applications.