An Asynchronous Federated Learning Arbitration Model for Low-Rate DDoS Attack Detection
Low-rate Distributed Denial of Service (LDDoS) attacks have been one of the most notorious network read more security threats, which use periodic slight multi-variate time series pulse flows to degrade network quality.Limited by the poor data in a single client, a powerful and satisfactory LDDoS attack detection model is hard to be trained.Federate