SF-AE: Split Federated Autoencoder for Unsupervised IoT Intrusion Detection

Abstract

Smart systems have become increasingly popular in recent years, widening the attack surface of cyber threats. Machine learning algorithms have been successfully integrated into modern security mechanisms to detect such attacks. Internet of Things (IoT) systems often have limited computational resources and are unable to execute entire machine learning pipelines. However, these systems often produce and manage sensitive data. Thus, it is preferable to avoid exposing their data to external analysis, e.g., on cloud systems. This work introduces SF-AE: a novel architecture that enables the distributed training of an anomaly-based intrusion detection system on devices with limited computational resources without exposing sensitive data. Experimental results on multiple datasets show that SF-AE outperforms state-of-the-art methods in terms of attack detection performance, at lower computation and communication costs for the participating devices