Practical side-channel based model extraction attack on tree-based machine learning algorithm
Machine learning algorithms have been widely applied to solve various type of problems and applications. Among those, decision tree based algorithms have been considered for small Internet-of-Things (IoT) implementation, due to their simplicity. It has been shown in a recent publication, that Bonsai...
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sg-ntu-dr.10356-1474252021-08-10T06:00:26Z Practical side-channel based model extraction attack on tree-based machine learning algorithm Jap, Dirmanto Yli-Mäyry, Ville Ito, Akira Ueno, Rei Bhasin, Shivam Homma, Naofumi 1st ACNS Workshop on Artificial Intelligence in Hardware Security (AIHWS 2020) Temasek Laboratories @ NTU Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Machine Learning Algorithms Side-channel Attacks Machine learning algorithms have been widely applied to solve various type of problems and applications. Among those, decision tree based algorithms have been considered for small Internet-of-Things (IoT) implementation, due to their simplicity. It has been shown in a recent publication, that Bonsai, a small tree-based algorithm, can be successfully fitted in a small 8-bit microcontroller. However, the security of machine learning algorithm has also been a major concern, especially with the threat of secret parameter recovery which could lead to breach of privacy. With machine learning taking over a significant proportion of industrial tasks, the security issue has become a matter of concern. Recently, secret parameter recovery for neural network based algorithm using physical side-channel leakage has been proposed. In the paper, we investigate the security of widely used decision tree algorithms running on ARM Cortex M3 platform against electromagnetic (EM) side-channel attacks. We show that by focusing on each building block function or component, one could perform divide-and-conquer approach to recover the secret parameters. To demonstrate the attack, we first report the recovery of secret parameters of Bonsai, such as, sparse projection parameters, branching function and node predictors. After the recovery of these parameters, the attacker can then reconstruct the whole architecture. This work was performed in the Cooperative Research Project of the Research Institute of Electrical Communication, Tohoku University with Nanyang Technological University. This research was also supported in part by JST CREST Grant No. JPMJCR19K5, Japan. 2021-08-10T06:00:26Z 2021-08-10T06:00:26Z 2020 Conference Paper Jap, D., Yli-Mäyry, V., Ito, A., Ueno, R., Bhasin, S. & Homma, N. (2020). Practical side-channel based model extraction attack on tree-based machine learning algorithm. 1st ACNS Workshop on Artificial Intelligence in Hardware Security (AIHWS 2020), LNCS 12418, 93-105. https://dx.doi.org/10.1007/978-3-030-61638-0_6 9783030616373 https://hdl.handle.net/10356/147425 10.1007/978-3-030-61638-0_6 2-s2.0-85094109821 LNCS 12418 93 105 en © 2020 Springer Nature Switzerland AG. All rights reserved. |
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Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Machine Learning Algorithms Side-channel Attacks Jap, Dirmanto Yli-Mäyry, Ville Ito, Akira Ueno, Rei Bhasin, Shivam Homma, Naofumi Practical side-channel based model extraction attack on tree-based machine learning algorithm |
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Machine learning algorithms have been widely applied to solve various type of problems and applications. Among those, decision tree based algorithms have been considered for small Internet-of-Things (IoT) implementation, due to their simplicity. It has been shown in a recent publication, that Bonsai, a small tree-based algorithm, can be successfully fitted in a small 8-bit microcontroller. However, the security of machine learning algorithm has also been a major concern, especially with the threat of secret parameter recovery which could lead to breach of privacy. With machine learning taking over a significant proportion of industrial tasks, the security issue has become a matter of concern. Recently, secret parameter recovery for neural network based algorithm using physical side-channel leakage has been proposed. In the paper, we investigate the security of widely used decision tree algorithms running on ARM Cortex M3 platform against electromagnetic (EM) side-channel attacks. We show that by focusing on each building block function or component, one could perform divide-and-conquer approach to recover the secret parameters. To demonstrate the attack, we first report the recovery of secret parameters of Bonsai, such as, sparse projection parameters, branching function and node predictors. After the recovery of these parameters, the attacker can then reconstruct the whole architecture. |
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1st ACNS Workshop on Artificial Intelligence in Hardware Security (AIHWS 2020) |
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1st ACNS Workshop on Artificial Intelligence in Hardware Security (AIHWS 2020) Jap, Dirmanto Yli-Mäyry, Ville Ito, Akira Ueno, Rei Bhasin, Shivam Homma, Naofumi |
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Conference or Workshop Item |
author |
Jap, Dirmanto Yli-Mäyry, Ville Ito, Akira Ueno, Rei Bhasin, Shivam Homma, Naofumi |
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Jap, Dirmanto |
title |
Practical side-channel based model extraction attack on tree-based machine learning algorithm |
title_short |
Practical side-channel based model extraction attack on tree-based machine learning algorithm |
title_full |
Practical side-channel based model extraction attack on tree-based machine learning algorithm |
title_fullStr |
Practical side-channel based model extraction attack on tree-based machine learning algorithm |
title_full_unstemmed |
Practical side-channel based model extraction attack on tree-based machine learning algorithm |
title_sort |
practical side-channel based model extraction attack on tree-based machine learning algorithm |
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2021 |
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https://hdl.handle.net/10356/147425 |
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