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WaC: First Results on Practical Side-Channel Attacks on Commercial Machine Learning Accelerator

Published: 15 November 2021 Publication History

Abstract

Commercial machine learning accelerators like Intel neural Compute Stick 2 (NCS2) enable efficient inference on otherwise low resource edge devices. However, these accelerators are also exposed to new threats leveraging physical access. In this paper, we present the first results demonstrating practical electromagnetic side-channel attack on NCS2, allowing secret weight recovery from executed models.

References

[1]
Lejla Batina, Shivam Bhasin, Dirmanto Jap, and Stjepan Picek. 2019. {CSI}{NN}: Reverse Engineering of Neural Network Architectures Through Electromagnetic Side Channel. In 28th {USENIX}Security Symposium ({USENIX}Security 19). 515--532.
[2]
Eric Brier, Christophe Clavier, and Francis Olivier. 2004. Correlation Power Analysis with a Leakage Model. In Cryptographic Hardware and Embedded Systems - CHES 2004: 6th International Workshop Cambridge, MA, USA, August 11--13, 2004. Proceedings (Lecture Notes in Computer Science, Vol. 3156), Marc Joye and Jean- Jacques Quisquater (Eds.). Springer, 16--29. https://doi.org/10.1007/978-3-540-28632-5_2
[3]
Nicholas Carlini, Matthew Jagielski, and Ilya Mironov. 2020. Cryptanalytic Extraction of Neural Network Models. In Advances in Cryptology - CRYPTO 2020 - 40th Annual International Cryptology Conference, CRYPTO 2020, Santa Barbara, CA, USA, August 17-21, 2020, Proceedings, Part III (Lecture Notes in Computer Science, Vol. 12172), Daniele Micciancio and Thomas Ristenpart (Eds.). Springer, 189--218. https://doi.org/10.1007/978-3-030-56877-1_7
[4]
Lukasz Chmielewski and Léo Weissbart. [n.d.]. On Reverse Engineering Neural Network Implementation on GPU. ([n. d.]).
[5]
Christian Doerr. 2018. Side-Channel Based Intrusion Detection for Industrial Control Systems. In Critical Information Infrastructures Security: 12th International Conference, CRITIS 2017, Lucca, Italy, October 8-13, 2017, Revised Selected Papers, Vol. 10707. Springer, 207.
[6]
Anuj Dubey, Rosario Cammarota, and Aydin Aysu. 2020. Maskednet: The first hardware inference engine aiming power side-channel protection. In 2020 IEEE International Symposium on Hardware Oriented Security and Trust (HOST). IEEE, 197--208.
[7]
Vasisht Duddu, Debasis Samanta, D. Vijay Rao, and Valentina E. Balas. 2018. Stealing Neural Networks via Timing Side Channels. CoRR abs/1812.11720 (2018). arXiv:1812.11720 http://arxiv.org/abs/1812.11720
[8]
Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio. 2016. Binarized Neural Networks. In Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain, Daniel D. Lee, Masashi Sugiyama, Ulrike von Luxburg, Isabelle Guyon, and Roman Garnett (Eds.). 4107--4115. https://proceedings.neurips.cc/paper/2016/hash/ d8330f857a17c53d217014ee776bfd50-Abstract.html
[9]
Intel. 2018. Neural Compute Stick 2. https://software.intel.com/content/www/ us/en/develop/hardware/neural-compute-stick.html.
[10]
Paul C. Kocher, Joshua Jaffe, and Benjamin Jun. 1999. Differential Power Analysis. In Advances in Cryptology - CRYPTO '99, 19th Annual International Cryptology Conference, Santa Barbara, California, USA, August 15-19, 1999, Proceedings (Lecture Notes in Computer Science, Vol. 1666), Michael J. Wiener (Ed.). Springer, 388--397. https://doi.org/10.1007/3-540-48405-1_25
[11]
Lingxiao Wei, Bo Luo, Yu Li, Yannan Liu, and Qiang Xu. 2018. I Know What You See: Power Side-Channel Attack on Convolutional Neural Network Accelerators. In Proceedings of the 34th Annual Computer Security Applications Conference, ACSAC 2018, San Juan, PR, USA, December 03-07, 2018. ACM, 393--406. https: //doi.org/10.1145/3274694.3274696
[12]
Yoo-Seung Won, Soham Chatterjee, Dirmanto Jap, Shivam Bhasin, and Arindam Basu. 2021. Time to Leak: Cross-Device Timing Attack On Edge Deep Learning Accelerator. In International Conference on Electronics, Information, and Communication, ICEIC 2021, Jeju, South Korea, January 31 - February 3, 2021. IEEE, 1--4. https://doi.org/10.1109/ICEIC51217.2021.9369754
[13]
Ville Yli-Mäyry, Akira Ito, Naofumi Homma, Shivam Bhasin, and Dirmanto Jap. 2021. Extraction of Binarized Neural Network Architecture and Secret Parameters Using Side-Channel Information. In 2021 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE, 1--5.

Cited By

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  • (2023)ID-Based Ring Signature against Continual Side Channel AttackSymmetry10.3390/sym1501017915:1(179)Online publication date: 7-Jan-2023
  • (2023)SystemC Model of Power Side-Channel Attacks Against AI Accelerators: Superstition or not?2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD)10.1109/ICCAD57390.2023.10323687(1-8)Online publication date: 28-Oct-2023
  • (2023)NNLeak: An AI-Oriented DNN Model Extraction Attack through Multi-Stage Side Channel Analysis2023 Asian Hardware Oriented Security and Trust Symposium (AsianHOST)10.1109/AsianHOST59942.2023.10409396(1-6)Online publication date: 13-Dec-2023
  • Show More Cited By

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      cover image ACM Conferences
      ASHES '21: Proceedings of the 5th Workshop on Attacks and Solutions in Hardware Security
      November 2021
      123 pages
      ISBN:9781450386623
      DOI:10.1145/3474376
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Publication History

      Published: 15 November 2021

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      Author Tags

      1. intel neural compute stick 2
      2. machine learning accelerator
      3. side-channel attack

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      Cited By

      View all
      • (2023)ID-Based Ring Signature against Continual Side Channel AttackSymmetry10.3390/sym1501017915:1(179)Online publication date: 7-Jan-2023
      • (2023)SystemC Model of Power Side-Channel Attacks Against AI Accelerators: Superstition or not?2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD)10.1109/ICCAD57390.2023.10323687(1-8)Online publication date: 28-Oct-2023
      • (2023)NNLeak: An AI-Oriented DNN Model Extraction Attack through Multi-Stage Side Channel Analysis2023 Asian Hardware Oriented Security and Trust Symposium (AsianHOST)10.1109/AsianHOST59942.2023.10409396(1-6)Online publication date: 13-Dec-2023
      • (2022)Anonymous Identity Based Broadcast Encryption against Continual Side Channel Attacks in the State Partition ModelApplied Sciences10.3390/app1218939512:18(9395)Online publication date: 19-Sep-2022
      • (2022)On (in)Security of Edge-based Machine Learning Against Electromagnetic Side-channels2022 IEEE International Symposium on Electromagnetic Compatibility & Signal/Power Integrity (EMCSI)10.1109/EMCSI39492.2022.9889639(262-267)Online publication date: 1-Aug-2022
      • (2022)High-Fidelity Model Extraction Attacks via Remote Power Monitors2022 IEEE 4th International Conference on Artificial Intelligence Circuits and Systems (AICAS)10.1109/AICAS54282.2022.9869973(328-331)Online publication date: 13-Jun-2022

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