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Article

Can machine learning be secure?

Authors:

Anthony D. Joseph,

J. D. TygarAuthors Info & Claims

ASIACCS '06: Proceedings of the 2006 ACM Symposium on Information, computer and communications security

Pages 16 - 25

https://doi.org/10.1145/1128817.1128824

Published: 21 March 2006 Publication History

Abstract

Machine learning systems offer unparalled flexibility in dealing with evolving input in a variety of applications, such as intrusion detection systems and spam e-mail filtering. However, machine learning algorithms themselves can be a target of attack by a malicious adversary. This paper provides a framework for answering the question, "Can machine learning be secure?" Novel contributions of this paper include a taxonomy of different types of attacks on machine learning techniques and systems, a variety of defenses against those attacks, a discussion of ideas that are important to security for machine learning, an analytical model giving a lower bound on attacker's work function, and a list of open problems.

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Cited By

Imam N(2024)Adversarial Examples on XAI-Enabled DT for Smart Healthcare SystemsSensors10.3390/s2421689124:21(6891)Online publication date: 27-Oct-2024
https://doi.org/10.3390/s24216891
Khazane HRidouani MSalahdine FKaabouch N(2024)A Holistic Review of Machine Learning Adversarial Attacks in IoT NetworksFuture Internet10.3390/fi1601003216:1(32)Online publication date: 19-Jan-2024
https://doi.org/10.3390/fi16010032
Moshawrab MAdda MBouzouane AIbrahim HRaad A(2024)Securing Federated Learning: Approaches, Mechanisms and OpportunitiesElectronics10.3390/electronics1318367513:18(3675)Online publication date: 16-Sep-2024
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Index Terms

Can machine learning be secure?
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
2. Security and privacy
  1. Systems security
    1. Operating systems security

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Information & Contributors

Information

Published In

cover image ACM Conferences

ASIACCS '06: Proceedings of the 2006 ACM Symposium on Information, computer and communications security

March 2006

384 pages

ISBN:1595932720

DOI:10.1145/1128817

Conference Chair:
Ferng-Ching Lin
Executive Yuan, Taiwan
,
General Chairs:
Der-Tsai Lee
Academia Sinica, Taiwan
,
Bao-Shuh Lin
ITRI, Taiwan
,
Program Chairs:
Shiuhpyng Shieh
National Chiao Tung University, Taiwan
,
Sushil Jajodia
George Mason University, USA

Copyright © 2006 ACM.

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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SIGSAC: ACM Special Interest Group on Security, Audit, and Control

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New York, NY, United States

Publication History

Published: 21 March 2006

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Asia CCS06

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Asia CCS06: ACM Symposium on Information, Computer and Communications Security 2006

March 21 - 24, 2006

Taipei, Taiwan

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Overall Acceptance Rate 418 of 2,322 submissions, 18%

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Cited By

Imam N(2024)Adversarial Examples on XAI-Enabled DT for Smart Healthcare SystemsSensors10.3390/s2421689124:21(6891)Online publication date: 27-Oct-2024
https://doi.org/10.3390/s24216891
Khazane HRidouani MSalahdine FKaabouch N(2024)A Holistic Review of Machine Learning Adversarial Attacks in IoT NetworksFuture Internet10.3390/fi1601003216:1(32)Online publication date: 19-Jan-2024
https://doi.org/10.3390/fi16010032
Moshawrab MAdda MBouzouane AIbrahim HRaad A(2024)Securing Federated Learning: Approaches, Mechanisms and OpportunitiesElectronics10.3390/electronics1318367513:18(3675)Online publication date: 16-Sep-2024
https://doi.org/10.3390/electronics13183675
Kim S(2024)Compact Data Learning for Machine Learning ClassificationsAxioms10.3390/axioms1303013713:3(137)Online publication date: 21-Feb-2024
https://doi.org/10.3390/axioms13030137
Gallego VNaveiro RRedondo ARíos Insua DRuggeri F(2024)Protecting Classifiers from AttacksStatistical Science10.1214/24-STS92239:3Online publication date: 1-Aug-2024
https://doi.org/10.1214/24-STS922
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Wang TLi FZhu LLi JZhang ZShen H(2024)Invisible Black-Box Backdoor Attack against Deep Cross-Modal Hashing RetrievalACM Transactions on Information Systems10.1145/365020542:4(1-27)Online publication date: 2-Mar-2024
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Durai Raju AWang KSerra ESpezzano F(2024)Low Carbon Footprint Training for 1D-CNNs with Temporal Max-PoolingProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679678(549-559)Online publication date: 21-Oct-2024
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Jonnalagadda AMohanty DZakee AKamalov F(2024)Modelling Data Poisoning Attacks Against Convolutional Neural NetworksJournal of Information & Knowledge Management10.1142/S021964922450022923:02Online publication date: 1-Feb-2024
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Guo WMa XGe PChen YYue QZhang Y(2024)Defense Contrastive Poisoning: An Application of JPEG to Self-Supervised Contrastive Learning Indiscriminate Poisoning Attacks2024 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing & Communications (GreenCom) and IEEE Cyber, Physical & Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics62450.2024.00097(505-510)Online publication date: 19-Aug-2024
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