MATEC Web Conf.
Volume 108, 20172017 International Conference on Mechanical, Aeronautical and Automotive Engineering (ICMAA 2017)
|Number of page(s)||4|
|Published online||31 May 2017|
Detecting Attacks in CyberManufacturing Systems: Additive Manufacturing Example
1 263 Link Hall, Department of Mechanical and Aerospace Engineering, Syracuse University, Syracuse NY 13244, USA
2 15 Francisco Alexandre Almeida - Vila Westin, UNIFAE, São João da Boa Vista SP 13870-377, Brasil
CyberManufacturing System is a vision for future manufacturing where physical components are fully integrated with computational processes in a connected environment. However, realizing the vision requires that its security be adequately ensured. This paper presents a vision-based system to detect intentional attacks on additive manufacturing processes, utilizing machine learning techniques. Particularly, additive manufacturing systems have unique vulnerabilities to malicious attacks, which can result in defective infills but without affecting the exterior. In order to detect such infill defects, the research uses simulated 3D printing process images as well as actual 3D printing process images to compare accuracies of machine learning algorithms in classifying, clustering and detecting anomalies on different types of infills. Three algorithms - (i) random forest, (ii) k nearest neighbor, and (iii) anomaly detection - have been adopted in the research and shown to be effective in detecting such defects.
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.