A URL attack detection method, wherein the method comprises: separately extracting features of multiple dimensions from information comprised in multiple URL access request samples, wherein none of the multiple URL access request samples are labeled with a sample label; constructing multiple training samples based on the extracted features; and training using the multiple training samples based on the Isolation Forest machine learning algorithm to obtain the URL attack detection model, wherein the URL attack detection model comprises M random binary trees obtained through training based on the Isolation Forest machine learning algorithm; and the training using the multiple training samples based on the Isolation Forest machine learning algorithm to obtain the URL attack detection model comprises: constructing M training sample subsets based on training samples uniformly sampled from the multiple training samples; randomly selecting a classification feature for each training sample subset from the features of the multiple dimensions as a root node, and randomly selecting a classification threshold value for each training sample subset from a value range between the maximum value and the minimum value of the classification feature; separately classifying training samples whose values of the classification feature are greater than or equal to the classification threshold value and training samples whose values of the classification feature are less than the classification threshold value in each training sample set under leaf nodes of the root node; using training samples in each leaf node as a new training sample subset to iteratively execute the above classification process until training samples in each obtained leaf node are no longer classifiable; extracting features of multiple dimensions from information comprised in a URL access request; entering the extracted features to a predetermined URL attack detection model for prediction calculation to obtain a risk score of the URL access request, wherein the URL attack detection model is a machine learning model obtained through training based on the Isolation Forest machine learning algorithm; and determining whether the URL access request is a URL attack request based on the risk score. separately extracting features of multiple dimensions from information comprised in multiple URL access request samples, wherein none of the multiple URL access request samples are labeled with a sample label; constructing multiple training samples based on the extracted features; and training using the multiple training samples based on the Isolation Forest machine learning algorithm to obtain the URL attack detection model, wherein the URL attack detection model comprises M random binary trees obtained through training based on the Isolation Forest machine learning algorithm; and the training using the multiple training samples based on the Isolation Forest machine learning algorithm to obtain the URL attack detection model comprises: constructing M training sample subsets based on training samples uniformly sampled from the multiple training samples; randomly selecting a classification feature for each training sample subset from the features of the multiple dimensions as a root node, and randomly selecting a classification threshold value for each training sample subset from a value range between the maximum value and the minimum value of the classification feature; separately classifying training samples whose values of the classification feature are greater than or equal to the classification threshold value and training samples whose values of the classification feature are less than the classification threshold value in each training sample set under leaf nodes of the root node; constructing M training sample subsets based on training samples uniformly sampled from the multiple training samples; randomly selecting a classification feature for each training sample subset from the features of the multiple dimensions as a root node, and randomly selecting a classification threshold value for each training sample subset from a value range between the maximum value and the minimum value of the classification feature; separately classifying training samples whose values of the classification feature are greater than or equal to the classification threshold value and training samples whose values of the classification feature are less than the classification threshold value in each training sample set under leaf nodes of the root node; using training samples in each leaf node as a new training sample subset to iteratively execute the above classification process until training samples in each obtained leaf node are no longer classifiable; extracting features of multiple dimensions from information comprised in a URL access request; entering the extracted features to a predetermined URL attack detection model for prediction calculation to obtain a risk score of the URL access request, wherein the URL attack detection model is a machine learning model obtained through training based on the Isolation Forest machine learning algorithm; and determining whether the URL access request is a URL attack request based on the risk score.