A novel magnetic current based Surface-Volume-Surface Electric Field Integral Equation (SVS-EFIE-M) is presented for the problem of scattering on homogeneous non-magnetic dielectric objects. The exact Galerkin Method of Moments (MoM) utilizing both the rotational and irrotational vector spherical harmonics as orthogonal basis and test functions according to the Helmholtz decomposition is implemented to solve SVS-EFIE-M analytically for the case of dielectric sphere excited by an electric dipole.


This dataset contains multimodal sensor data collected from side-channels while printing several types of objects on an Ultimaker 3 3D printer. Our related research paper titled "Sabotage Attack Detection for Additive Manufacturing Systems" can be found here: https://doi.org/10.1109/ACCESS.2020.2971947. In our work, we demonstrate that this sensor data can be used with machine learning algorithms to detect sabotage attacks on the 3D printer.


Design and fabrication outsourcing has made integrated circuits vulnerable to malicious modifications by third parties known as hardware Trojan (HT). Over the last decade, the use of side-channel measurements for detecting the malicious manipulation of the chip has been extensively studied. However, the suggested approaches mostly suffer from two major limitations: reliance on trusted identical chip (e.i. golden chip); untraceable footprints of subtle hardware Trojans which remain inactive during the testing phase.