Session: 06-07-01- Sustainable design

Paper Number: 100224

100224 - Blockchain-Enabled Cyber-Physical Security Protection for Advanced Manufacturing 

With the rapid development and broad adoption of the Internet of Things (IoT) and information technologies, more and more advanced manufacturing systems become cyber-enabled, such as the popular additive manufacturing systems. Facilitated by the cyber-enabled system design, the flexibility and productivity of manufacturing can be significantly improved. However, the cyber-enabled environment may pose the manufacturing system under high risks of cyber-physical attacks as well. Particularly, the critical manufacturing data, such as the design data and process data, will become risky due to the vulnerabilities that have been identified in the literature. In general, the cyber-physical attacks may maliciously tamper the data to alter the critical information or illegally access the confidential data without authorization. Data tampering could result in alteration of the desired product properties as well as the false alarms or failures of process anomaly detection. On the other hand, unauthorized access would cause the leakage of key information and intellectual property. Therefore, there is an urgent need in developing effective approaches to protect the data security from these attacks in the cyber-enabled manufacturing systems.

To achieve this goal, inspired by the recent advances of the emerging blockchain technology, an integrative blockchain-enabled manufacturing data security protection methodology is proposed in this study. In this methodology, to enable high resistance against data tampering, the blockchain architecture is integrated to store the vulnerable data which are strictly ordered, such as the G-code and online streaming sensor data. Meanwhile, to prevent unauthorized data access, an asymmetry encryption-based approach is also leveraged to encrypted data and integrated it with the employed blockchain architecture. In addition, a novel data camouflage technique is proposed to convert the ciphertext to a format that is similar to the raw data to further reduce the potential attacks.

Two preliminary real-world case studies in additive manufacturing, including the design data attack and online sensor data attack, were conducted to validate the effectiveness of the proposed blockchain-enabled cyber-physical security protection methodology. The results demonstrate that malicious tampering could be detected in a relatively short time and the risk of unauthorized data access is significantly reduced as well. Thus, the proposed method has great potential to effectively protect the security for cyber-physical manufacturing systems. It is also worth mentioning that the proposed method does not have significant conflicts with the existing common cyber security protection techniques, e.g., anomaly detection algorithms. So, the proposed method can also be directly integrated with the existing techniques to further eliminate the risk of cyber-physical attacks.

Presenting Author: Chenang Liu Oklahoma State University

Presenting Author Biography: Dr. Chenang Liu is an Assistant Professor in the School of Industrial Engineering and Management at Oklahoma State University. He earned his Ph.D. degree in Industrial and Systems Engineering from Virginia Tech in 2019. He also received his master’s degree in Statistics from Virginia Tech in 2017 and double bachelor's degrees from Zhejiang University in 2014. His research interests include sensor fusion and data-driven analytics for advanced manufacturing, process quality monitoring and control methodologies, and artificial intelligence-enabled techniques for smart manufacturing and healthcare applications.

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