Secure adaptive federated learning for scalable anomaly detection in industrial IoT networks

The rapid expansion of Industrial Internet of Things (IIoT) infrastructures has increased the demand for robust, scalable, and privacy-preserving intrusion and anomaly detection mechanisms. Traditional centralized detection systems face critical limitations, including high privacy risks, communication bottlenecks, and vulnerability to adversarial tampering. To address these challenges, we propose SAFNet-IoT, a Secure Adaptive Federated Network that integrates Blockchain-Based Authentication (BBA) with an Adaptive Autoencoder-LSTM (AAEL) anomaly detection module for decentralized IIoT environments. The BBA mechanism ensures secure model update validation and integrity preservation through smart contracts, effectively mitigating malicious model manipulation. Meanwhile, the AAEL module dynamically adjusts its hyperparameters based on real-time feedback to enhance detection accuracy under heterogeneous and non-stationary IIoT conditions. Experimental results demonstrate that SAFNet-IoT achieves 94.7% anomaly detection accuracy, surpassing conventional federated and deep learning baselines such as FedAVG-LSTM (93.1%) and deep autoencoders (91.2%). The framework additionally reduces communication overhead by 30%, achieves a bandwidth reduction ratio of 0.67, and maintains an average local training time of 1.4 s with an end-to-end system latency of 4.6 s, enabling deployment on resource-constrained IIoT nodes. The blockchain layer provides 98.5% authentication success and 91.2% malicious update detection, outperforming traditional secure aggregation schemes. These results highlight SAFNet-IoT as an effective and resilient intrusion and anomaly detection solution for next-generation IIoT environments. Future work will focus on optimizing smart contract execution costs and extending SAFNet-IoT toward multi-modal data fusion and large-scale, real-time anomaly detection.