TY - GEN
T1 - Offramps
T2 - 54th Annual IEEE/IFIP International Conference on Dependable Systems and Networks, DSN 2024
AU - Blocklove, Jason
AU - Raz, Md
AU - Roy, Prithwish Basu
AU - Pearce, Hammond
AU - Krishnamurthy, Prashanth
AU - Khorrami, Farshad
AU - Karri, Ramesh
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Cybersecurity threats in Additive Manufacturing (AM) are an increasing concern as AM adoption continues to grow. AM is now being used for parts in the aerospace, transportation, and medical domains. Threat vectors which allow for part compromise are particularly concerning, as any failure in these domains would have life-threatening consequences. A major challenge to investigation of AM part-compromises comes from the difficulty in evaluating and benchmarking both identified threat vectors as well as methods for detecting adversarial actions. In this work, we introduce a generalized platform for systematic analysis of attacks against and defenses for 3D printers. Our 'OFFRAMPS' platform is based on the open-source 3D printer control board 'RAMPS.' Offramps allows analysis, recording, and modification of all control signals and I/O for a 3D printer. We show the efficacy of Offramps by presenting a series of case studies based on several Trojans, including ones identified in the literature, and show that Offramps can both emulate and detect these attacks, i.e., it can both change and detect arbitrary changes to the g-code print commands.
AB - Cybersecurity threats in Additive Manufacturing (AM) are an increasing concern as AM adoption continues to grow. AM is now being used for parts in the aerospace, transportation, and medical domains. Threat vectors which allow for part compromise are particularly concerning, as any failure in these domains would have life-threatening consequences. A major challenge to investigation of AM part-compromises comes from the difficulty in evaluating and benchmarking both identified threat vectors as well as methods for detecting adversarial actions. In this work, we introduce a generalized platform for systematic analysis of attacks against and defenses for 3D printers. Our 'OFFRAMPS' platform is based on the open-source 3D printer control board 'RAMPS.' Offramps allows analysis, recording, and modification of all control signals and I/O for a 3D printer. We show the efficacy of Offramps by presenting a series of case studies based on several Trojans, including ones identified in the literature, and show that Offramps can both emulate and detect these attacks, i.e., it can both change and detect arbitrary changes to the g-code print commands.
KW - Additive Manufacturing
KW - Cybersecurity
UR - http://www.scopus.com/inward/record.url?scp=85203794432&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85203794432&partnerID=8YFLogxK
U2 - 10.1109/DSN58291.2024.00057
DO - 10.1109/DSN58291.2024.00057
M3 - Conference contribution
AN - SCOPUS:85203794432
T3 - Proceedings - 2024 54th Annual IEEE/IFIP International Conference on Dependable Systems and Networks, DSN 2024
SP - 535
EP - 543
BT - Proceedings - 2024 54th Annual IEEE/IFIP International Conference on Dependable Systems and Networks, DSN 2024
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 24 June 2024 through 27 June 2024
ER -