Neuromorphic Federated Learning Framework for Real-Time DDoS Attack Detection in Distributed Networks

Abstract

Distributed Denial of Service (DDoS) attacks pose a serious threat to network infrastructure, necessitating real-time detection mechanisms that can operate within distributed environments without compromising data privacy. This paper presents a novel neuromorphic federated learning system that combines bio-inspired spiking neural networks (SNNs) with a federated learning architecture for self-ambient DDoS detection within distributed networks. Our approach employs stateful Leaky Integrate-and-Fire (LIF) neurons with sufficient membrane potential dynamics, Poisson rate encoding for handling temporal information, and network-centric communication protocols. The architecture was evaluated on the CIC-DDoS2019 dataset on five federated nodes with uniform data distribution. Experimental findings reveal 96.64% accuracy, 99.83% precision, 93.44% recall, and 99.65% ROC-AUC score. The system demonstrates real-time performance with an average latency of 0.797ms, a P95 latency of 0.859ms, and a throughput of 1,254.67 samples/second, meeting the critical 100ms requirement for real-time intrusion detection. The federated architecture accommodates collaborative learning without centralising sensitive network data, with an overall communication overhead of 108.13 MB over 20 rounds of training. Our neuromorphic solution offers a promising solution to energy-efficient, privacy-preserved DDoS detection in modern distributed network environments.