ETSI TR 104 222 V1.2.1 (2024-07)

Securing Artificial Intelligence; Mitigation Strategy Report

RTR/SAI-009

General Information

• Status: Not Published
• Technical Committee: SAI - Securing Artificial Intelligence TC
• Current Stage: 12 - Citation in the OJ (auto-insert)
• Due Date: 15-Jul-2024
• Completion Date: 10-Jul-2024

Standards Content (Sample)

TECHNICAL REPORT
Securing Artificial Intelligence;
Mitigation Strategy Report
2 ETSI TR 104 222 V1.2.1 (2024-07)

Reference
RTR/SAI-009
Keywords
artificial intelligence, security

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ETSI
3 ETSI TR 104 222 V1.2.1 (2024-07)
Contents
Intellectual Property Rights . 4
Foreword . 4
Modal verbs terminology . 4
1 Scope . 5
2 References . 5
2.1 Normative references . 5
2.2 Informative references . 5
3 Definition of terms, symbols and abbreviations . 10
3.1 Terms . 10
3.2 Symbols . 11
3.3 Abbreviations . 11
4 Overview . 11
4.1 Machine learning models workflow . 11
4.2 Mitigation strategy framework . 12
5 Mitigations against training attacks . 13
5.1 Introduction . 13
5.2 Mitigating poisoning attacks . 14
5.2.1 Overview . 14
5.2.2 Model enhancement mitigations against poisoning attacks . 15
5.2.3 Model-agnostic mitigations against poisoning attacks. 15
5.3 Mitigating backdoor attacks . 16
5.3.1 Overview . 16
5.3.2 Model enhancement mitigations against backdoor attacks . 16
5.3.3 Model-agnostic mitigations against backdoor attacks . 17
6 Mitigations against inference attacks . 18
6.1 Introduction . 18
6.2 Mitigating evasion attacks . 19
6.2.1 Overview . 19
6.2.2 Model enhancement mitigations against evasion attacks . 20
6.2.3 Model-agnostic mitigations against evasion attacks . 22
6.3 Mitigating model stealing . 24
6.3.1 Overview . 24
6.3.2 Model enhancement mitigations against model stealing. 25
6.3.3 Model-agnostic mitigations against model stealing . 25
6.4 Mitigating data extraction . 26
6.4.1 Overview . 26
6.4.2 Model enhancement mitigations against data extraction . 27
6.4.3 Model-agnostic mitigations against data extraction . 28
7 Conclusion . 28
History . 29

ETSI
4 ETSI TR 104 222 V1.2.1 (2024-07)
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Foreword
This Technical Report (TR) has been produced by ETSI Technical Committee Securing Artificial Intelligence (SAI).
Modal verbs terminology
In the present document "should", "should not", "may", "need not", "will", "will not", "can" and "cannot" are to be
interpreted as described in clause 3.2 of the ETSI Drafting Rules (Verbal forms for the expression of provisions).
"must" and "must not" are NOT allowed in ETSI deliverables except when used in direct citation.

ETSI
5 ETSI TR 104 222 V1.2.1 (2024-07)
1 Scope
The present document summarizes and analyses existing and potential mitigation against threats for AI-based systems
as discussed in ETSI GR SAI 004 [i.1]. The goal is to have a technical survey for mitigating against threats introduced
by adopting AI into systems. The technical survey shed light on available methods of securing AI-based systems by
mitigating against known or potential security threats. It also addresses security capabilities, challenges, and limitations
when adopting mitigation for AI-based systems in certain potential use cases.
2 References
2.1 Normative references
Normative references are not applicable in the present document.
2.2 Informative references
References are either specific (identified by date of publication and/or edition number or version number) or
non-specific. For specific references, only the cited version applies. For non-specific references, the latest version of the
referenced document (including any amendments) applies.
NOTE: While any hyperlinks included in this clause were valid at the time of publication, ETSI cannot guarantee
their long term validity.
The following referenced documents are not necessary for the application of the present document but they assist the
user with regard to a particular subject area.
[i.1] ETSI GR SAI 004: "Securing Artificial Intelligence (SAI); Problem Statement".
[i.2] Doyen Sahoo, Quang Pham, Jing Lu and Steven C. H. Hoi: "Online Deep Learning: Learning
Deep Neural Networks on the Fly", International Joint Conferences on Artificial Intelligence
Organization, 2018.
[i.3] Battista Biggio and Fabio Roli: "Wild patterns: Ten years after the rise of adversarial machine
learning", Pattern Recognition, 2018.
[i.4] Qiang Liu, Pan Li, Wentao Zhao, Wei Cai, Shui Yu and Victor C. M. Leung: "A Survey on
Security Threats and Defensive Techniques of Machine Learning: A Data Driven View".
IEEE Access 2018.
[i.5] Nicolas Papernot, Patrick D. McDaniel, Arunesh Sinha and Michael P. Wellman: "SoK: Security
and Privacy in Machine Learning". IEEE European Symposium on Security and Privacy
(EuroS&P) 2018.
[i.6] Han Xu, Yao Ma, Haochen Liu, Debayan Deb, Hui Liu, Jiliang Tang and Anil K. Jain:
"Adversarial Attacks and Defenses in Images, Graphs and Text: A Review". International Journal
of Automation and Computing volume 17, pages151-178(2020).
[i.7] Anirban Chakraborty, Manaar Alam, Vishal Dey, Anupam Chattopadhyay and Debdeep
Muk
...