Research on Anomaly Detection and Privacy Protection of Network Security Data Based on Machine Learning

With the increasingly complex network security situation, the traditional protection technology is gradually unable to cope with new and changeable network attacks. The anomaly detection method based on machine learning has become an important tool to improve the network security protection ability. With the wide application of cloud computing, how to give full play to the advantages of machine learning under the premise of protecting data privacy has become an important issue in current research. To solve this challenge, a privacy protection scheme based on homomorphic encryption is proposed to ensure the privacy and security of user data by anomaly detection of encrypted data without affecting the detection accuracy. In order to solve the format mismatch problem of encrypted data, this paper also designs a format verification mechanism to detect and correct format errors before data upload to ensure the accuracy of the training process. The experimental results show that this method can achieve the same detection effect as the traditional method while protecting privacy and has a good application prospect. It not only provides a feasible privacy protection scheme for the field of network security, but also provides a valuable reference for other fields involving sensitive data processing.