Confidentiality Breach Through Acoustic Side-Channel in Cyber-Physical Additive Manufacturing Systems

Abstract

In cyber-physical systems, due to the tight integration of the computational, communication, and physical components, most of the information in the cyber-domain manifests in terms of physical actions (such as motion, temperature change, etc.). This leads to the system being prone to physical-to-cyber domain attacks that affect the confidentiality. Physical actions are governed by energy flows, which may be observed. Some of these observable energy flows unintentionally leak information about the cyber-domain and hence are known as the side-channels. Side-channels such as acoustic, thermal, and power allow attackers to acquire the information without actually leveraging the vulnerability of the algorithms implemented in the system. As a case study, we have taken cyber-physical additive manufacturing systems (fused deposition modeling-based three-dimensional (3D) printer) to demonstrate how the acoustic side-channel can be used to breach the confidentiality of the system. In 3D printers, geometry, process, and machine information are the intellectual properties, which are stored in the cyber domain (G-code). We have designed an attack model that consists of digital signal processing, machine-learning algorithms, and context-based post processing to steal the intellectual property in the form of geometry details by reconstructing the G-code and thus the test objects. We have successfully reconstructed various test objects with an average axis prediction accuracy of 86% and an average length prediction error of 11.11%.