FIDS: Detecting DDoS Through Federated Learning Based Method
Jingyi Li, Zikai Zhang, Yidong Li, Xinyue Guo, Huifang Li
Abstract
Recently, federated learning has been used by Network Intrusion Detection Systems (NIDSs) to expanding data features while preserving data privacy. However, non-independent and identically distributed (non-iid) datasets weaken the model performance of federated learning. In this paper, we present a novel Federated Intrusion Detection System(FIDS) to classify the differences of DDoS attacks from the non-iid dataset. A prototypical weight is introduced to measure the correlations between global data space and local data spaces. We then explore the feature combinations of abnormal behaviors and extract extra features from original data in preprocessing steps. Experimental results show that the FIDS improves the performance of training stability and convergence rate compared to two baselines.