STEALTHY PROCESS ATTACK DETECTION FOR AUTOMATED MANUFACTURING

Additive manufacturing's reliance on embedded computing renders it vulnerable to tampering through cyber-attacks. Sensor instrumentation of additive manufacturing devices allows for rigorous process and security monitoring, but also results in a massive volume of noisy data for each run. As suc...

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Bibliographische Detailangaben
Hauptverfasser:	Ericson, Milton N, Yoginath, Srikanth B, Tansakul, Varisara, Jordan, Robert C, Dawson, Joel A, Iannacone, Michael D, Long, Gavin B, Passian, Ali, Asiamah, Joel M
Format:	Patent
Sprache:	eng
Schlagworte:	ADDITIVE MANUFACTURING TECHNOLOGY ADDITIVE MANUFACTURING, i.e. MANUFACTURING OFTHREE-DIMENSIONAL [3-D] OBJECTS BY ADDITIVE DEPOSITION, ADDITIVEAGGLOMERATION OR ADDITIVE LAYERING, e.g. BY 3-D PRINTING,STEREOLITHOGRAPHY OR SELECTIVE LASER SINTERING AFTER-TREATMENT OF THE SHAPED PRODUCTS, e.g. REPAIRING CALCULATING COMPUTING COUNTING ELECTRIC DIGITAL DATA PROCESSING PERFORMING OPERATIONS PHYSICS SHAPING OF MATERIAL IN A PLASTIC STATE, NOT OTHERWISE PROVIDEDFOR SHAPING OR JOINING OF PLASTICS TRANSPORTING WORKING OF PLASTICS WORKING OF SUBSTANCES IN A PLASTIC STATE, IN GENERAL
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Beschreibung
Zusammenfassung:	Additive manufacturing's reliance on embedded computing renders it vulnerable to tampering through cyber-attacks. Sensor instrumentation of additive manufacturing devices allows for rigorous process and security monitoring, but also results in a massive volume of noisy data for each run. As such, in-situ, near-real-time anomaly detection is challenging. A probabilistic-model-based approach addresses this challenge.