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Threat Vector Analysis - Finding Fault in the Pile

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2021 Fall ASEE Middle Atlantic Section Meeting


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Publication Date

November 12, 2021

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November 12, 2021

End Date

November 13, 2021

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Caleb Ian-Watson Beckwith CUNY New York City College of Technology

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I am a Senior in mechanical engineering at the New York City College of Technology in Brooklyn New York. Over the past three years, I have worked with my school and several others both inside and outside of the US in order to research and learn more about Additive Manufacturing and how it is incorporated with the engineering supply chain and design process. This includes working with NYU over the summer as part of their NSF IRES summer research program with students from India to learn how cyber security plays a role in AM and how machine learning can be used to combat cyber/physical attacks,

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Threat Vector Analysis - Finding Fault in the Pile

Caleb Beckwith1, Harsh Sankar Naicker2, Svara Mehta3, Viba R Udupa4, Nghia Tri Nim5, Varun Gadre6, Hammond Pearce7, Gary Mac7

1 NYC College of Technology, 300 Jay St, Brooklyn, NY 11201 2 Vellore Institute of Technology, Kelambakkam - Vandalur Rd, Rajan Nagar, Chennai, Tamil Nadu 600127, India 3 Indian Institute of Technology, Goa Engineering College Campus, Farmagudi, Ponda, Goa 403401, India 4 National Institute of Technology NH 66, Srinivasnagar, Surathkal, Mangalore, Karnataka 575025, India 5 New York University Abu Dhabi, Abu Dhabi, UAE. 6 Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh 208016, India. 7 New York University Tandon School of Engineering, 6 MetroTech Center, Brooklyn, NY 11201


In the additive manufacturing (AM) supply chain, there are numerous factors that may allow malicious third parties to negatively influence the specific outcomes of a given product. These factors are known as threat vectors, and commonly include avenues for counterfeiting, information leakage, and sabotage. To determine the specific risks within a given production line, product designers should perform risk analyses to study the likelihood of various attacks. These can be visualized as heat maps, which then allow for designers to address possible attackers and determine the likelihood and vector they may begin navigating. To that effect, a case study was examined in order to address possible sabotage attacks. Attackers seeking to sabotage may introduce malicious files into groups of benign files. For AM one such case where this applies is with G-Code files, and through the application of machine learning the malicious files can be detected through feature recognition in the code seeing how the altered ones deviate from the acceptable ones thus finding the fault(s) in the pile.

Beckwith, C. I. (2021, November), Threat Vector Analysis - Finding Fault in the Pile Paper presented at 2021 Fall ASEE Middle Atlantic Section Meeting, Virtually Hosted by the section.

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