Echo State Network Learning for the Detection of Cyber Attacks in Additive Manufacturing

Owing to its layer-by-layer nature, additive manufacturing (AM) has been leveraged in various industries for the fabrication of parts with complex geometries. In the era of the Industrial Internet of Things (IIoT), AM processes are increasingly casted into both cyber and physical domains. As such, it poses AM under high risks of cyber attacks, leading to altered AM parts with potentially compromised mechanical properties and functionalities. It is imperative to develop new methodologies for the detection of cyber attacks for quality and reliability assurance of products in cyber-physical AM processes. Based on the echo state network, an online monitoring approach is developed in this study to extract features from side channels for the detection of cyber attacks. Two real-world case studies are conducted to evaluate the proposed approach on the fused filament fabrication (FFF) process. Experimental results have shown that the proposed approach is effective in identifying abnormities induced by different types of cyber attacks. The proposed approach has a strong potential to be extended to other AM processes with various sensing and monitoring devices.