Brief Paper: Investigating the Possibility of Manufactured Product Reconstruction Using Side-Channels – A Systematic Literature Review

Abstract With recent advancements in computer and network technologies, Additive Manufacturing (AM), together with other manufacturing processes, is subject to cyberattacks. In AM, feedstock materials in powder, fluid, or wire forms are deposited on the build plates layer by layer to build structures along the 3D digital models. One challenge with AM is protecting intellectual property due to the way design information is concealed within the object’s geometry. Furthermore, even with well-placed security measures, adversaries can exploit key parameters that influence part geometry during the printing process through side-channel emissions leakage. This leakage can be used to reconstruct parts through advanced analysis methods for signals from indirect sources, enabling attackers to reverse-engineer a part’s 3D geometry. The goal of this work is to assess the prevalence of research efforts focusing on reconstructing manufactured products using side-channels (auxiliary process-related) information, which will be achieved through a three-phase systematic literature review within both the Compendex and Scopus databases. The finally selected articles were analyzed to understand 1) the type of used side-channels with different manufacturing processes, 2) state-of-the-art techniques in reconstructing manufactured parts, and 3) existing research opportunities in this area. More specifically, the papers were analyzed from different perspectives, and several insights were obtained pertaining to: 1) the type of side-channels used, 2) targeted manufacturing processes, and 3) the implementation of reconstruction techniques. For instance, it was found that a variety of side-channel types have been used, with acoustics being the most common. In addition, it was noticed that most of the articles used Fused Deposition Modeling (FDM) desktop printers, perhaps due to their ability to rapidly and cost-effectively prototype complex, lightweight, and free-form 3D objects. However, one study did attempt to reconstruct an object using both additive and subtractive processes, suggesting that part reconstruction could be applied across various manufacturing processes and may not be limited to AM. Another insight is that reconstruction has utilized different machine learning and deep learning approaches to enhance the accuracy of reconstructed objects, including random forests and neural networks. Based on this literature review, several recommendations are made to encourage more research in this area. A recommended avenue is to consider manufacturing processes beyond AM, including subtractive processes. Additionally, reducing signal leakages should be pursued, especially as new advances in cyber-physical technologies emerge. Despite the useful insights obtained from this review, this work is not without limitations. The results of this analysis may not necessarily be generalizable; thus, examining more publications would be beneficial. Hence, future work will apply forward and backward tracking techniques to uncover additional studies.