Federated autoencoder IDS for IoT: A Fed-ANIDS approach using CICIoT2023

Intrusion detection on Internet of Things (IoT) networks is still a challenging task because of the vast quantity of data, evolving attack patterns, and strong privacy controls across the scattered systems. Even though the current deep learning and Transformer-based intrusion detection systems are effective, numerous IoT nodes cannot offer the centralized data aggregate or processing capabilities that these systems require. This paper introduces Fed-ANIDS, a federated autoencoder-based intrusion detection system enhanced with feature selection by using Learning-based Intelligent Intrusion Detection (LBIID) and a High-performance ViT Intrusion Detection System (HiViT-IDS) classifier. This model is based on a lightweight feature-selection module to reduce duplication before classification, autoencoder to generate small latent representations, and a federated training approach to safeguard data privacy. The complete analysis of the CICIoT2023 dataset allows concluding that the proposed solution has a high detection rate and a significantly reduced inference latency and overhead in communication. The results confirm the suitability of the proposed methodology to real-time, scalable and privacy protection IoT intrusion detection. The accuracy of the system was 99.84% with precision and recall at 98.86%, F1-score at 98.85% and specificity at 98.02%.