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Stealthy Cyber Anomaly Detection On Large Noisy Multi-mater..:

, In: Proceedings of the 2022 ACM CCS Workshop on Additive Manufacturing (3D Printing) Security,

Yoginath, Srikanth ; Iannacone, Michael ; Tansakul, Varisara... - p. 25-38 , 2022

Link: https://dl.acm.org/doi/10.1145/3560833.3563564

RT 
T1 <cTitle>Proceedings of the 2022 ACM CCS Workshop on Additive Manufacturing (3D Printing) Security</cTitle>: T1 <Title>Stealthy Cyber Anomaly Detection On Large Noisy Multi-material 3D Printer Datasets Using Probabilistic Models</Title>

UL https://suche.suub.uni-bremen.de/peid=acm-3563564&Exemplar=1&LAN=DE
				
A1 Yoginath, Srikanth
A1 Iannacone, Michael
A1 Tansakul, Varisara
A1 Passian, Ali
A1 Jordan, Rob
A1 Asiamah, Joel
A1 Ericson, M. Nance
A1 Long, Gavin
A1 Dawson, Joel A.
PB ACM
YR 2022
K1 additive layer manufacturing
K1 anomaly detection
K1 bayesian inference
K1 cyber-physical system security
K1 markov chain monte carlo
K1 Security and privacy
K1 Intrusion/anomaly detection and malware mitigation
K1 Intrusion detection systems
K1 Security in hardware
K1 Hardware attacks and countermeasures
K1 Side-channel analysis and countermeasures
K1 Embedded systems security
SP 25
OP 38
LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3560833.3563564
DO https://dl.acm.org/doi/10.1145/3560833.3563564
SF ELIB - SuUB Bremen

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