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Identifying Cyber-Physical Vulnerabilities in Additive Manufacturing Systems using a Systems Approach | IEEE Conference Publication | IEEE Xplore

Identifying Cyber-Physical Vulnerabilities in Additive Manufacturing Systems using a Systems Approach


Abstract:

The increasing influence and adoption of Additive Manufacturing (AM) technology across manufacturing sectors has made it a target for cyber-physical attacks. While severa...Show More

Abstract:

The increasing influence and adoption of Additive Manufacturing (AM) technology across manufacturing sectors has made it a target for cyber-physical attacks. While several techniques have been developed to mitigate specific AM related threats, there is little research aimed at assessing cyber-physical threats or vulnerabilities that extend across the entire AM workflow. Such an assessment requires a holistic approach that systematically analyzes all components of the AM workflow for cyber-physical vulnerabilities. Several methodologies have been successfully applied towards identifying such vulnerabilities in other domains like Information Technology (IT) systems. In response, this paper provides a systems approach towards identifying cyber-physical vulnerabilities in AM systems using the Vulnerability Assessment and Mitigation (VAM) methodology. This approach characterizes the different vulnerabilities that arise from specific AM threats by identifying the quality attributes of the AM system that introduces it. The security techniques developed to mitigate these threats are reduced to a combination of fundamental mitigation techniques, that have been compiled based on its success in other domains. Using the resources from the VAM methodology, fundamental mitigation techniques that are best suited towards mitigating specific vulnerability attributes are identified. Comparing the combination of fundamental mitigation techniques associated with an AM security technique and the list of fundamental mitigation techniques suggested by the VAM methodology provides insight into how an AM security technique can be improved. Finally, the paper provides a case study of the proposed adapted VAM methodology to demonstrate its application.
Date of Conference: 11-14 October 2020
Date Added to IEEE Xplore: 14 December 2020
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Conference Location: Toronto, ON, Canada

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