Design and Implementation of Web Application Anomaly Detection System Based on SVM
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.
support vector machineSVMabnormal detectionWeb application security