Paper Info
Title
Sabotage Attack Detection for Additive Manufacturing Systems
Abstract
This paper presents a novel multi-modal sabotage attack detection system for Additive Manufacturing (AM) machines. By utilizing multiple side-channels, we improve system state estimation significantly in comparison to uni-modal techniques. Besides, we analyze the value of each side-channel for performing attack detection in terms of mutual information shared with the machine control parameters. We evaluate our system on real-world test cases and achieve an attack detection accuracy of 98.15%. AM, or 3D Printing, is seeing practical use for the rapid prototyping and production of industrial parts. The digitization of such systems not only makes AM a crucial technology in Industry 4.0 but also presents a broad attack surface that is vulnerable to kinetic cyberattacks. In the field of AM security, sabotage attacks are cyberattacks that introduce inconspicuous defects to a manufactured component at any specific process of the AM digital process chain, resulting in the compromise of the component's structural integrity and load-bearing capabilities. Defense mechanisms that detect such attacks using side-channel analysis have been studied. However, most current works focus on modeling the state of AM systems using a single side-channel, thus limiting their effectiveness at attack detection. In this paper, we demonstrate the value of a multi-modal sabotage attack detection system in comparison to uni-modal techniques.
Year
DOI
Venue
2020
10.1109/ACCESS.2020.2971947
IEEE ACCESS
Keywords
DocType
Volume
Additive-manufacturing,cyber-physical systems security,side-channel analysis,3D-printer security,sabotage attack
Journal
8
ISSN
Citations 
PageRank 
2169-3536
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Shih-Yuan Yu133.43
Arnav Vaibhav Malawade211.70
Sujit Rokka Chhetri3505.76
Mohammad Abdullah Al Faruque462765.35