Industrial Internet of Things Intrusion Detection System Based on Federated Learning

Abstract. With the rapid development of Industrial Internet of Things (IIoT), its secu-rity has become a focus of attention. Traditional centralized intrusion detection sys-tems (IDS) face challenges of privacy leakage and high communication overhead in IIoT environments. This article proposes an IIoT intrusion detection system based on federated learning (FL-IDS). The system introduces Paillier homomorphic encryption technology to enhance the security of data transmission, uses Bi-LSTM to extract network traffic data features, and uses Transformer for model training. The experi-mental results show that our system outperforms other models in terms of detection rate and false alarm rate. This framework effectively improves the accuracy of intru-sion detection, reduces communication bandwidth requirements, and protects user privacy while ensuring model convergence.