Poster Abstract: Thermal Side-Channel Forensics in Additive Manufacturing Systems

Abstract

Additive manufacturing systems leak cyber-related information (such as G-code, M-code, etc.) from the side-channels (such as acoustic, power, thermal, etc.). In our work, we have successfully demonstrated the vulnerability of additive manufacturing to thermal side-channel attacks, where confidentiality can be breached to steal the Intellectual Property (IP) in the form of 3D design and printing parameters. We introduce a novel methodology to reverse engineer the thermal images acquired from the thermal side-channel to extract specific information (such as speed, temperature, axis of movement, etc.) present in the cyber-domain. To the best of our knowledge, this kind of forensics has not yet been explored in additive manufacturing systems.