  Increasing usage of Digital Manufacturing (DM) in safety-critical domains is
increasing attention on the cybersecurity of the manufacturing process, as
malicious third parties might aim to introduce defects in digital designs. In
general, the DM process involves creating a digital object (as CAD files)
before using a slicer program to convert the models into printing instructions
(e.g. g-code) suitable for the target printer. As the g-code is an intermediate
machine format, malicious edits may be difficult to detect, especially when the
golden (original) models are not available to the manufacturer. In this work we
aim to quantify this hypothesis through a red-team/blue-team case study,
whereby the red-team aims to introduce subtle defects that would impact the
properties (strengths) of the 3D printed parts, and the blue-team aims to
detect these modifications in the absence of the golden models. The case study
had two sets of models, the first with 180 designs (with 2 compromised using 2
methods) and the second with 4320 designs (with 60 compromised using 6
methods). Using statistical modelling and machine learning (ML), the blue-team
was able to detect all the compromises in the first set of data, and 50 of the
compromises in the second.
