Information Leakage-Aware Computer-Aided Cyber-Physical Manufacturing

Cyber-physical additive manufacturing systems consist of tight integration of cyber and physical domains. This union, however, induces new cross-domain vulnerabilities that pose unique security challenges. One of these challenges is preventing confidentiality breach, caused by physical-to-cyber domain attacks. In this form of attack, attackers utilize the side-channels (such as acoustics, power, electromagnetic emissions, and so on) in the physical-domain to estimate and steal cyber-domain data (such as G/M-codes). Since these emissions depend on the physical structure of the system, one way to minimize the information leakage is to modify the physical-domain. However, this process can be costly due to added hardware modification. Instead, we propose a novel methodology that allows the cyber-domain tools [such as computer aided-manufacturing (CAM)] to be aware of the existing information leakage. Then, we propose to change either machine process or product design parameters in the cyber-domain to minimize the information leakage. Our methodology aids the existing cyber-domain and physical-domain security solution by utilizing the cross-domain relationship. We have implemented our methodology in a fused-deposition modeling-based Cartesian additive manufacturing system. Our methodology achieves reduction of mutual information by 24.94% in acoustic side-channel, 32.91% in power side-channel, 32.29% in magnetic side-channel, and 55.65% in vibration side-channel. As a case study, to help understand the implication of mutual information drop, we have also presented the calculation of success rate and the reconstruction of the 3D object based on an attack model. For the given attack model, our leakage-aware CAM tool decreases the success rate of an attacker by 8.74% and obstructs the reconstruction of finer geometry details.