Skip to main content

2021 | OriginalPaper | Buchkapitel

4. Security of AI Hardware Systems

verfasst von : Haoting Shen

Erschienen in: Emerging Topics in Hardware Security

Verlag: Springer International Publishing

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Artificial intelligence (AI) systems are changing our lives. Coming with the benefits, challenges on AI security raise concerns as AI systems are not only accessing personal and sensitive data, they are also to be deployed on systems related to life safety (e.g., autonomous vehicles and medical systems) and critical infrastructures. In this chapter, we will start from a brief introduction to modern AI systems, review reported AI security issues, and discuss possible countermeasures.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literatur
1.
Zurück zum Zitat I. Anati, S. Gueron, S. Johnson, V. Scarlata, Innovative technology for CPU based attestation and sealing, in Proceedings of the 2nd International Workshop on Hardware and Architectural Support for Security and Privacy, vol. 13 (ACM, New York, 2013), p. 7 I. Anati, S. Gueron, S. Johnson, V. Scarlata, Innovative technology for CPU based attestation and sealing, in Proceedings of the 2nd International Workshop on Hardware and Architectural Support for Security and Privacy, vol. 13 (ACM, New York, 2013), p. 7
3.
Zurück zum Zitat N. Baracaldo, B. Chen, H. Ludwig, J.A. Safavi, Mitigating poisoning attacks on machine learning models: a data provenance based approach, in Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security (2017), pp. 103–110 N. Baracaldo, B. Chen, H. Ludwig, J.A. Safavi, Mitigating poisoning attacks on machine learning models: a data provenance based approach, in Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security (2017), pp. 103–110
5.
Zurück zum Zitat B. Biggio, B. Nelson, P. Laskov, Poisoning attacks against support vector machines (preprint, 2012). arXiv:1206.6389 B. Biggio, B. Nelson, P. Laskov, Poisoning attacks against support vector machines (preprint, 2012). arXiv:1206.6389
6.
Zurück zum Zitat B. Biggio, I. Corona, D. Maiorca, B. Nelson, N. Šrndić, P. Laskov, G. Giacinto, F. Roli, Evasion attacks against machine learning at test time, in Joint European Conference on Machine Learning and Knowledge Discovery in Databases (Springer, Berlin, 2013), pp. 387–402 B. Biggio, I. Corona, D. Maiorca, B. Nelson, N. Šrndić, P. Laskov, G. Giacinto, F. Roli, Evasion attacks against machine learning at test time, in Joint European Conference on Machine Learning and Knowledge Discovery in Databases (Springer, Berlin, 2013), pp. 387–402
7.
Zurück zum Zitat B. Biggio, L. Didaci, G. Fumera, F. Roli, Poisoning attacks to compromise face templates, in 2013 International Conference on Biometrics (ICB) (IEEE, Piscataway, 2013), pp. 1–7CrossRef B. Biggio, L. Didaci, G. Fumera, F. Roli, Poisoning attacks to compromise face templates, in 2013 International Conference on Biometrics (ICB) (IEEE, Piscataway, 2013), pp. 1–7CrossRef
8.
Zurück zum Zitat B. Biggio, K. Rieck, D. Ariu, C. Wressnegger, I. Corona, G. Giacinto, F. Roli, Poisoning behavioral malware clustering, in Proceedings of the 2014 Workshop on Artificial Intelligent and Security Workshop (2014), pp. 27–36 B. Biggio, K. Rieck, D. Ariu, C. Wressnegger, I. Corona, G. Giacinto, F. Roli, Poisoning behavioral malware clustering, in Proceedings of the 2014 Workshop on Artificial Intelligent and Security Workshop (2014), pp. 27–36
9.
Zurück zum Zitat I. Damgård, V. Pastro, N. Smart, S. Zakarias, Multiparty computation from somewhat homomorphic encryption, in Annual Cryptology Conference (Springer, Berlin, 2012), pp. 643–662MATH I. Damgård, V. Pastro, N. Smart, S. Zakarias, Multiparty computation from somewhat homomorphic encryption, in Annual Cryptology Conference (Springer, Berlin, 2012), pp. 643–662MATH
10.
Zurück zum Zitat J. Dean, S. Ghemawat, MapReduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008)CrossRef J. Dean, S. Ghemawat, MapReduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008)CrossRef
12.
Zurück zum Zitat Y. Doröz, E. Öztürk, B. Sunar, Accelerating fully homomorphic encryption in hardware. IEEE Trans. Comput. 64(6), 1509–1521 (2014)MathSciNetMATH Y. Doröz, E. Öztürk, B. Sunar, Accelerating fully homomorphic encryption in hardware. IEEE Trans. Comput. 64(6), 1509–1521 (2014)MathSciNetMATH
13.
Zurück zum Zitat K. Eykholt, I. Evtimov, E. Fernandes, B. Li, A. Rahmati, C. Xiao, A. Prakash, T. Kohno, D. Song, Robust physical-world attacks on deep learning visual classification, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018), pp. 1625–1634 K. Eykholt, I. Evtimov, E. Fernandes, B. Li, A. Rahmati, C. Xiao, A. Prakash, T. Kohno, D. Song, Robust physical-world attacks on deep learning visual classification, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018), pp. 1625–1634
14.
Zurück zum Zitat M. Fredrikson, S. Jha, T. Ristenpart, Model inversion attacks that exploit confidence information and basic countermeasures, in Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security (2015), pp. 1322–1333 M. Fredrikson, S. Jha, T. Ristenpart, Model inversion attacks that exploit confidence information and basic countermeasures, in Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security (2015), pp. 1322–1333
15.
Zurück zum Zitat J.E. Gonzalez, Y. Low, H. Gu, D. Bickson, C. Guestrin, Powergraph: distributed graph-parallel computation on natural graphs, in Presented as part of the 10th USENIX Symposium on Operating Systems Design and Implementation (OSDI’12) (2012), pp. 17–30 J.E. Gonzalez, Y. Low, H. Gu, D. Bickson, C. Guestrin, Powergraph: distributed graph-parallel computation on natural graphs, in Presented as part of the 10th USENIX Symposium on Operating Systems Design and Implementation (OSDI’12) (2012), pp. 17–30
16.
Zurück zum Zitat I.J. Goodfellow, J. Shlens, C. Szegedy, Explaining and harnessing adversarial examples (preprint, 2014). arXiv:1412.6572 I.J. Goodfellow, J. Shlens, C. Szegedy, Explaining and harnessing adversarial examples (preprint, 2014). arXiv:1412.6572
17.
Zurück zum Zitat G. Hospodar, R. Maes, I. Verbauwhede, Machine learning attacks on 65nm Arbiter PUFs: accurate modeling poses strict bounds on usability, in 2012 IEEE International Workshop on Information Forensics and Security (WIFS) (IEEE, Piscataway, 2012), pp. 37–42 G. Hospodar, R. Maes, I. Verbauwhede, Machine learning attacks on 65nm Arbiter PUFs: accurate modeling poses strict bounds on usability, in 2012 IEEE International Workshop on Information Forensics and Security (WIFS) (IEEE, Piscataway, 2012), pp. 37–42
18.
Zurück zum Zitat N.P. Jouppi, C. Young, D.H. Yoon, et al., In-datacenter performance analysis of a tensor processing unit, in Proceedings of the 44th Annual International Symposium on Computer Architecture (ISCA’17) (ACM, New York, 2017), pp. 1–12. https://doi.org/10.1145/3079856.3080246 N.P. Jouppi, C. Young, D.H. Yoon, et al., In-datacenter performance analysis of a tensor processing unit, in Proceedings of the 44th Annual International Symposium on Computer Architecture (ISCA’17) (ACM, New York, 2017), pp. 1–12. https://​doi.​org/​10.​1145/​3079856.​3080246
19.
Zurück zum Zitat P.W. Koh, J. Steinhardt, P. Liang, Stronger data poisoning attacks break data sanitization defenses (preprint, 2018). arXiv:1811.00741 P.W. Koh, J. Steinhardt, P. Liang, Stronger data poisoning attacks break data sanitization defenses (preprint, 2018). arXiv:1811.00741
20.
Zurück zum Zitat P. Laskov, M. Kloft, A framework for quantitative security analysis of machine learning, in Proceedings of the 2nd ACM Workshop on Security and Artificial Intelligence (2009), pp. 1–4 P. Laskov, M. Kloft, A framework for quantitative security analysis of machine learning, in Proceedings of the 2nd ACM Workshop on Security and Artificial Intelligence (2009), pp. 1–4
21.
Zurück zum Zitat C. Liu, B. Li, Y. Vorobeychik, A. Oprea, Robust linear regression against training data poisoning, in Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security (2017), pp. 91–102 C. Liu, B. Li, Y. Vorobeychik, A. Oprea, Robust linear regression against training data poisoning, in Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security (2017), pp. 91–102
22.
Zurück zum Zitat Z. Mengchen, B. An, W. Gao, T. Zhang, Efficient label contamination attacks against black-box learning models, in (IJCAI) (2017), pp. 3945–3951 Z. Mengchen, B. An, W. Gao, T. Zhang, Efficient label contamination attacks against black-box learning models, in (IJCAI) (2017), pp. 3945–3951
23.
Zurück zum Zitat A.C. Mert, E. Öztürk, E. Savaş, Design and implementation of encryption/decryption architectures for BFV homomorphic encryption scheme. IEEE Trans. Very Large Scale Integr. Syst. 28(2), 353–362 (2019)CrossRef A.C. Mert, E. Öztürk, E. Savaş, Design and implementation of encryption/decryption architectures for BFV homomorphic encryption scheme. IEEE Trans. Very Large Scale Integr. Syst. 28(2), 353–362 (2019)CrossRef
24.
Zurück zum Zitat P.H. Nguyen, D.P. Sahoo, C. Jin, K. Mahmood, U. Rührmair, M. van Dijk, The interpose PUF: secure PUF design against state-of-the-art machine learning attacks. IACR Trans. Cryptograph. Hardware Embed. Syst. 4, 243–290 (2019)CrossRef P.H. Nguyen, D.P. Sahoo, C. Jin, K. Mahmood, U. Rührmair, M. van Dijk, The interpose PUF: secure PUF design against state-of-the-art machine learning attacks. IACR Trans. Cryptograph. Hardware Embed. Syst. 4, 243–290 (2019)CrossRef
25.
27.
Zurück zum Zitat R. Shokri, M. Stronati, C. Song, V. Shmatikov, Membership inference attacks against machine learning models, in 2017 IEEE Symposium on Security and Privacy (SP) (IEEE, Piscataway, 2017), pp. 3–18 R. Shokri, M. Stronati, C. Song, V. Shmatikov, Membership inference attacks against machine learning models, in 2017 IEEE Symposium on Security and Privacy (SP) (IEEE, Piscataway, 2017), pp. 3–18
28.
Zurück zum Zitat V. Sundaresan, S. Rammohan, R. Vemuri, Defense against side-channel power analysis attacks on microelectronic systems, in 2008 IEEE National Aerospace and Electronics Conference (IEEE, Piscataway, 2008), pp. 144–150 V. Sundaresan, S. Rammohan, R. Vemuri, Defense against side-channel power analysis attacks on microelectronic systems, in 2008 IEEE National Aerospace and Electronics Conference (IEEE, Piscataway, 2008), pp. 144–150
29.
Zurück zum Zitat N. Šrndic, P. Laskov, Detection of malicious PDF files based on hierarchical document structure, in Proceedings of the 20th Annual Network & Distributed System Security Symposium (Citeseer, 2013), pp. 1–16 N. Šrndic, P. Laskov, Detection of malicious PDF files based on hierarchical document structure, in Proceedings of the 20th Annual Network & Distributed System Security Symposium (Citeseer, 2013), pp. 1–16
30.
Zurück zum Zitat L. Wei, B. Luo, Y. Li, Y. Liu, Q. Xu, I know what you see: power side-channel attack on convolutional neural network accelerators, in Proceedings of the 34th Annual Computer Security Applications Conference (2018), pp. 393–406 L. Wei, B. Luo, Y. Li, Y. Liu, Q. Xu, I know what you see: power side-channel attack on convolutional neural network accelerators, in Proceedings of the 34th Annual Computer Security Applications Conference (2018), pp. 393–406
31.
Zurück zum Zitat G.L. Wittel, S.F. Wu, On attacking statistical spam filters, in First Conference on Email and Anti-Spam CEAS (2004) G.L. Wittel, S.F. Wu, On attacking statistical spam filters, in First Conference on Email and Anti-Spam CEAS (2004)
32.
Zurück zum Zitat G. Xu, H. Li, H. Ren, K. Yang, R.H. Deng, Data security issues in deep learning: attacks, countermeasures, and opportunities. IEEE Commun. Mag. 57(11), 116–122 (2019)CrossRef G. Xu, H. Li, H. Ren, K. Yang, R.H. Deng, Data security issues in deep learning: attacks, countermeasures, and opportunities. IEEE Commun. Mag. 57(11), 116–122 (2019)CrossRef
33.
Zurück zum Zitat C. Yang, Q. Wu, H. Li, Y. Chen, Generative poisoning attack method against neural networks (preprint, 2017). arXiv:1703.01340 C. Yang, Q. Wu, H. Li, Y. Chen, Generative poisoning attack method against neural networks (preprint, 2017). arXiv:1703.01340
34.
Zurück zum Zitat M. Zaharia, M. Chowdhury, T. Das, A. Dave, J. Ma, M. McCauly, M.J. Franklin, S. Shenker, I. Stoica, Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing, in Presented as Part of the 9th USENIX Symposium on Networked Systems Design and Implementation (NSDI’12) (2012), pp. 15–28 M. Zaharia, M. Chowdhury, T. Das, A. Dave, J. Ma, M. McCauly, M.J. Franklin, S. Shenker, I. Stoica, Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing, in Presented as Part of the 9th USENIX Symposium on Networked Systems Design and Implementation (NSDI’12) (2012), pp. 15–28
35.
Zurück zum Zitat J.-L. Zhang, G. Qu, Y.-Q. Lv, Q. Zhou, A survey on silicon PUFs and recent advances in ring oscillator PUFs. J. Comput. Sci. Technol. 29(4), 664–678 (2014)CrossRef J.-L. Zhang, G. Qu, Y.-Q. Lv, Q. Zhou, A survey on silicon PUFs and recent advances in ring oscillator PUFs. J. Comput. Sci. Technol. 29(4), 664–678 (2014)CrossRef
Metadaten
Titel
Security of AI Hardware Systems
verfasst von
Haoting Shen
Copyright-Jahr
2021
DOI
https://doi.org/10.1007/978-3-030-64448-2_4

Neuer Inhalt