Abstract: Disclosed are a URL parameter value anomaly detection method and system. The method includes obtaining a real-time request log; analyzing the real-time request log to generate one or more parameter values; classifying each parameter value based on a preset parameter value classification category to determine a parameter value category of a request URL in the real-time request log; generating a key according to a domain name, a request URI, and a parameter name in the real-time request log; retrieving a target dataset corresponding to the key from a pre-stored parameter feature library; and matching the parameter value category of the request URL in the real-time request log with a category and a category confidence level of the target dataset; and when they are not matched, it can be determined that the parameter value of the request URL in the real-time request log is anomalous.