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dc.contributor.authorRott, Michal
dc.contributor.authorMonroy, Sergio A.Salinas
dc.identifier.citationRott M., Monroy S.A.S. (2021) Power-Based Intrusion Detection for Additive Manufacturing: A Deep Learning Approach. In: Peñalver L., Parra L. (eds) Industrial IoT Technologies and Applications. Industrial IoT 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 365. Springer, Cham.
dc.descriptionClick on the URL link to access this conference paper on the publisher’s website (may not be free.)en_US
dc.description.abstractDue to the ability of 3D-printers to build a wide range of objects at low costs, many industries are rapidly adopting additive manufacturing. However due to their sensing and communications capabilities, 3D-printers are Internet of Things (IoT) devices that are vulnerable to sophisticated cyberattacks, such as defect injection attacks. By maliciously manipulating the behavior of a 3D-printer, an attacker can compromise the integrity of a manufactured objects. To avoid detection, the adversary also compromises the sensor data reported by the 3D-printer that the operator could use to detect the attack. In this paper, we design a deep neural network that can detect such attacks by predicting the power consumption of a 3D-printer based on the object design and previous power consumption observations. By analyzing the difference between the predicted power consumption and the observed one, we can determine if the 3D-printer is under attack. By measuring the power consumption of the 3D-printer at the power line with an independent sensor, we can determine the true behavior of the 3D-printer without relying on sensor data reported by the potentially compromised 3D-printer. Compared to previous works, our proposed detection technique only requires cheap power sensors that can be easily installed. We conduct extensive experiments on a real-world additive manufacturing testbed and observe that our proposed method can detect defect injection attacks with up to 96% accuracy.en_US
dc.publisherSpringer, Chamen_US
dc.relation.ispartofseriesLecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering;Vol. 365
dc.subjectIntrusion detectionen_US
dc.subjectSide-channel defenseen_US
dc.subjectAdditive manufacturingen_US
dc.titlePower-based intrusion detection for additive manufacturing: A deep learning approachen_US
dc.typeConference paperen_US
dc.rights.holder© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2021en_US

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