Web Malicious Request Identification Based on k-Nearest Neighbor Algorithm

Cybersecurity issues are becoming more and more serious in the Internet era, and malicious requests are the main threat. In order to effectively deal with this problem, this study uses the K-Nearest Neighbor algorithm to conduct an in-depth study of the KDD CUP dataset. By converting character features into numerical features and normalizing the data, we successfully simplify the complex data into a form that can be understood by the model. The dataset is divided into training and testing sets for model training and evaluation to ensure the accuracy of the results. Eventually, we built an efficient malicious request detection model, which brings new insights and solutions to the field of network security protection.