An Asynchronous Federated Learning Arbitration Model for Low-Rate DDoS Attack Detection
Zengguang Liu, Cuiyun Guo, D. Liu, Xiaochun Yin
Abstract
Low-rate Distributed Denial of Service (LDDoS) attacks have been one of the most notorious network 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. Federated Learning (FL) is a promising paradigm offering joint learning through multiple clients. We propose an asynchronous federated learning arbitration framework based on bidirectional LSTM (bi-LSTM) and attention mechanism (AsyncFL-bLAM). In the AsyncFL-bLAM, the leader node election algorithm is proposed for constructing the framework of asynchronous federated learning. The proposed bLAM model composed of feature extracter and arbitrator takes on the responsibility of LDDoS detection locally. Furthermore, the novel AsyncFL framework helps to upload and aggregate the bLAM models’ parameters asynchronously between leader node and client nodes. Experimental results show that the AsyncFL-bLAM outperforms the state-of-the-art models in accuracy, and reduces the overall communication rounds.