Detection and Intrusion of Attacks in Cyber-physical Security for Additive Manufacturing
Type of DegreePhD Dissertation
Industrial and Systems Engineering
MetadataShow full item record
Cyber-physical systems (CPS) have become increasingly prevalent in industrial production as they integrate sensing, computation, control, and networking into physical objects and infrastructure. One of the branches of CPS, additive manufacturing (AM) – also known as 3D printing – enables the fabrication of geometrically precise items layer by layer. This technology has revolutionized the manufacturing industry by allowing for more efficient and cost-effective production of complex and customized parts. However, the widespread integration of physical facilities with the internet has amplified the risk of malicious activity, leaving entire systems vulnerable to cyber threats. As a result, concerns over security breaches in CPS within the Internet of Things (IoT) have escalated. While the security challenges in AM are multi-fold, this research specifically focuses on detecting cyber-physical threats and performing a side-channel attack to reconstruct the model, which may result in the theft of Intellectual Property (IP). By providing different contributions to solving these issues, the research aims to enhance the security of CPS and prevent unauthorized access, theft, or tampering of sensitive information. With side-channel power monitoring, a novel intrusion detection method is proposed to counter threats in cyber-physical manufacturing systems. One of the potential malicious attacks in this context is the covert insertion of voids during printing, which can have severe consequences. To address this challenge, we propose a novel power-monitoring model based on Dynamic Time Warping (DTW) to detect malicious activity in a polymer AM process. Our results demonstrate that this approach not only facilitates rapid alteration detection compared to the other methods but also enables precise identification of void location down to a specific layer. Furthermore, we have extended the application of the model to another machine, enabling us to verify the print’s authentication remotely. A physical-to-cyber domain attack is when information gathered from the physical domain is exploited to reveal sensitive information about the cyber domain. To illustrate the vulnerability of AM to such attacks, we propose a novel method for reconstructing the geometric form of a model using side-channel information obtained from the rotation of the motors. Our research highlights the need for preventive measures against Intellectual Property (IP) theft in AM and reveals that the model has been restored, closely matching the original CAD design. This study contributes to the subject of the security domain in cyber-physical manufacturing systems, with an emphasis on intrusion detection as well as protection against possible vulnerabilities. Some limitations and future works are also provided here as proof of concept for further expansion into other security topics in CPS.