Power-based intrusion detection for additive manufacturing: A deep learning approach

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Authors
Rott, Michael
Monroy, Sergio A.Salinas
Issue Date
2021-03-11
Type
Conference paper
Language
en_US
Keywords
Security , Intrusion detection , Side-channel defense , 3D printing , Additive manufacturing
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Abstract

Due 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.

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Citation
Rott 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. https://doi.org/10.1007/978-3-030-71061-3_11
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Springer, Cham
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