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.
Highlights
Additive Manufacturing (AM), or 3D Printing, is a manufacturing process that constructs a 3D physical object layer-bylayer according to its digital representation
We propose an attack detection system that continuously monitors and analyzes the side-channel information leaked during the operation of AM systems, allowing us to identify unusual analog emissions resulting from potential sabotage attacks
We describe our experimental setup (Section V-A), evaluate the mutual information shared between the control parameters and each modality (Section V-B), and present results for axis classification and velocity regression (Section V-C)
Summary
Additive Manufacturing (AM), or 3D Printing, is a manufacturing process that constructs a 3D physical object layer-bylayer according to its digital representation. NOVEL CONTRIBUTIONS Our previous work has tackled the research challenges mentioned above using acoustic side-channel information from a single sensor for sabotage attack detection [21] Another previous work has investigated the use of various analog emissions (vibration, acoustic, magnetic, and power) to breach the confidentiality of 3D printed objects and how machine parameters are leaked to these different side-channels [22]. AM systems leak information about machine parameters in the form of analog emissions to different side-channels because of the physical characteristics of their actuation and movement In this manner, a system that monitors and fuses all the available side-channel information can better model the state of an AM system and be a more accurate sabotage attack detection system. 4) Multi-Modal Sabotage Attack Detection System which uses multiple low-cost sensors to capture information from various side-channels and demonstrates better performance at sabotage attack detection compared to uni-modal techniques
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