{{위키데이터 속성 추적}}
{{기계 학습}}
'''적대적 기계 학습'''(Adversarial machine learning)은 [[기계 학습]] 알고리즘에 대한 공격과 그러한 공격에 대한 방어에 대한 연구이다.<ref>{{서적 인용|last1=Kianpour|first1=Mazaher|title=Intelligent Systems and Applications|last2=Wen|first2=Shao-Fang|date=2020|isbn=978-3-030-29515-8|series=Advances in Intelligent Systems and Computing|volume=1037|pages=111–125|language=en|chapter=Timing Attacks on Machine Learning: State of the Art|doi=10.1007/978-3-030-29516-5_10|s2cid=201705926}}</ref> 2020년 5월의 설문조사에 따르면 실무자들은 산업용 애플리케이션에서 기계 학습 시스템을 더 잘 보호해야 할 필요성이 절실하다고 보고했다.<ref name=":1">{{서적 인용|last1=Siva Kumar |first1=Ram Shankar |last2=Nyström |first2=Magnus |last3=Lambert |first3=John |last4=Marshall |first4=Andrew |last5=Goertzel |first5=Mario |last6=Comissoneru |first6=Andi |last7=Swann |first7=Matt |last8=Xia |first8=Sharon |title=2020 IEEE Security and Privacy Workshops (SPW) |chapter=Adversarial Machine Learning-Industry Perspectives |date=May 2020 |chapter-url=https://ieeexplore.ieee.org/document/9283867 |pages=69–75 |doi=10.1109/SPW50608.2020.00028|isbn=978-1-7281-9346-5 |s2cid=229357721 }}</ref>

대부분의 기계 학습 기술은 교육 및 테스트 데이터가 동일한 통계 분포(IID)에서 생성된다는 가정 하에 특정 문제 세트에서 작동하도록 대부분 설계되었다. 그러나 이 가정은 사용자가 통계적 가정을 위반하는 조작된 데이터를 의도적으로 제공할 수 있는 실제 고부담 애플리케이션에서 위험할 정도로 위반되는 경우가 많다.

적대적 기계 학습에서 가장 일반적인 공격에는 회피 공격<ref name="GoodfellowMcDaniel20182">{{저널 인용 |last1=Goodfellow |first1=Ian |last2=McDaniel |first2=Patrick |last3=Papernot |first3=Nicolas |date=25 June 2018 |title=Making machine learning robust against adversarial inputs |url=https://dl.acm.org/doi/10.1145/3134599 |journal=Communications of the ACM |language=en |volume=61 |issue=7 |pages=56–66 |doi=10.1145/3134599 |issn=0001-0782 |doi-access=free |access-date=2024-04-14 |archive-date=2023-10-28 |archive-url=https://web.archive.org/web/20231028111614/https://dl.acm.org/doi/10.1145/3134599 |url-status= }}</ref>, 데이터 중독 공격<ref>{{콘퍼런스 인용|conference=International Conference on Learning Representations 2021 |type=Poster |last1=Geiping |first1=Jonas |last2=Fowl |first2=Liam H. |last3=Huang |first3=W. Ronny |last4=Czaja |first4=Wojciech |last5=Taylor |first5=Gavin |last6=Moeller |first6=Michael |last7=Goldstein |first7=Tom |date=2020-09-28 |title=Witches' Brew: Industrial Scale Data Poisoning via Gradient Matching |url=https://openreview.net/forum?id=01olnfLIbD |language=en}}</ref>, [[비잔티움 장애 허용|비잔틴 공격]]<ref name=":13">{{저널 인용|last1=El-Mhamdi |first1=El Mahdi |last2=Farhadkhani   |first2=Sadegh |last3=Guerraoui |first3=Rachid |last4=Guirguis |first4=Arsany |last5=Hoang |first5=Lê-Nguyên |last6=Rouault |first6=Sébastien |date=2021-12-06 |title=Collaborative Learning in the Jungle (Decentralized, Byzantine, Heterogeneous, Asynchronous and Nonconvex Learning) |url=https://proceedings.neurips.cc/paper/2021/hash/d2cd33e9c0236a8c2d8bd3fa91ad3acf-Abstract.html |journal=Advances in Neural Information Processing Systems |language=en |volume=34|arxiv=2008.00742 }}</ref> 및 모델 추출이 포함된다.<ref>{{콘퍼런스 인용|conference=25th USENIX Security Symposium |last1=Tramèr |first1=Florian |last2=Zhang |first2=Fan |last3=Juels |first3=Ari |last4=Reiter |first4=Michael K. |last5=Ristenpart |first5=Thomas |date=2016 |title=Stealing Machine Learning Models via Prediction {APIs} |url=https://www.usenix.org/conference/usenixsecurity16/technical-sessions/presentation/tramer |language=en |pages=601–618 |isbn=978-1-931971-32-4}}</ref>

== 같이 보기 ==
* [[패턴 인식]]
* [[생성적 적대 신경망]]

== 각주 ==
{{각주}}

== 외부 링크 ==
* [https://atlas.mitre.org/ MITRE ATLAS: Adversarial Threat Landscape for Artificial-Intelligence Systems]
* [https://csrc.nist.gov/publications/detail/nistir/8269/draft NIST 8269 Draft: A Taxonomy and Terminology of Adversarial Machine Learning]
* NIPS 2007 Workshop on [https://web.archive.org/web/20120108072159/http://nips.cc/Conferences/2007/Program/event.php?ID=615 Machine Learning in Adversarial Environments for Computer Security]
* [http://pralab.diee.unica.it/en/ALFASVMLib AlfaSVMLib] {{Webarchive|url=https://web.archive.org/web/20200924221614/http://pralab.diee.unica.it/en/ALFASVMLib |date=2020-09-24 }} – Adversarial Label Flip Attacks against Support Vector Machines
* {{저널 인용|last1=Laskov|first1=Pavel|last2=Lippmann|first2=Richard|year=2010|title=Machine learning in adversarial environments|journal=Machine Learning|volume=81|issue=2|pages=115–119|doi=10.1007/s10994-010-5207-6|s2cid=12567278}}
* Dagstuhl Perspectives Workshop on "[https://www.dagstuhl.de/en/seminars/seminar-calendar/seminar-details/12371 Machine Learning Methods for Computer Security]"
* Workshop on [http://aisec.cc/ Artificial Intelligence and Security], (AISec) Series

[[분류:기계 학습]]
[[분류:컴퓨터 보안]]