Privacy-Preserving Federated Anomaly Detection Framework for Multi-Domain Network Security

With the rapid development of information technology, network security has increasingly become a key focus across various industries. In particular, in multi-domain network environments, network security not only involves traditional firewalls and intrusion detection systems, but also faces challenges in data privacy protection. Especially when data is shared and collaborated across multiple domains, the risk of privacy leakage increases. Therefore, how to enhance network security while ensuring data privacy protection has become a pressing issue to address. This paper proposes a privacy-preserving federated anomaly detection framework that combines federated learning technology with privacy protection mechanisms, aiming to achieve efficient anomaly detection through collaboration across multiple domains while protecting data privacy. The paper details the core modules of the framework, including local model training, parameter update and aggregation mechanisms, as well as the privacy protection module. To balance anomaly detection efficiency with privacy protection, the framework employs advanced machine learning algorithms and performs multi-domain data fusion. Through experimental evaluation, the framework is shown to improve anomaly detection accuracy while effectively ensuring data privacy. Additionally, the framework is designed with strong scalability, making it applicable to various practical application scenarios such as enterprise networks, the Internet of Things, and cloud computing. This research provides an innovative solution for multi-domain network security and offers future research directions, including further optimization of framework performance, strengthening privacy protection technologies, and exploring application cases.