research-article

Is data clustering in adversarial settings secure?

Authors:

Battista Biggio,

Ignazio Pillai,

Samuel Rota Bulò,

Marcello Pelillo,

Fabio RoliAuthors Info & Claims

AISec '13: Proceedings of the 2013 ACM workshop on Artificial intelligence and security

Pages 87 - 98

https://doi.org/10.1145/2517312.2517321

Published: 04 November 2013 Publication History

Abstract

Clustering algorithms have been increasingly adopted in security applications to spot dangerous or illicit activities. However, they have not been originally devised to deal with deliberate attack attempts that may aim to subvert the clustering process itself. Whether clustering can be safely adopted in such settings remains thus questionable. In this work we propose a general framework that allows one to identify potential attacks against clustering algorithms, and to evaluate their impact, by making specific assumptions on the adversary's goal, knowledge of the attacked system, and capabilities of manipulating the input data. We show that an attacker may significantly poison the whole clustering process by adding a relatively small percentage of attack samples to the input data, and that some attack samples may be obfuscated to be hidden within some existing clusters. We present a case study on single-linkage hierarchical clustering, and report experiments on clustering of malware samples and handwritten digits.

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Cited By

Zheng YLin CLyu XZhou XLi GWang T(2024)Robustness of Updatable Learning-based Index Advisors against Poisoning AttackProceedings of the ACM on Management of Data10.1145/36392652:1(1-26)Online publication date: 26-Mar-2024
https://dl.acm.org/doi/10.1145/3639265
Rass SKönig SAhmad SGoman M(2024)Metricizing the Euclidean Space Toward Desired Distance Relations in Point CloudsIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.342024619(7304-7319)Online publication date: 2024
https://doi.org/10.1109/TIFS.2024.3420246
Mongardini ALa Morgia MJajodia SVincenzo Mancini LMei A(2024)DARD: Deceptive Approaches for Robust Defense Against IP TheftIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.340243319(5591-5606)Online publication date: 2024
https://doi.org/10.1109/TIFS.2024.3402433
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Index Terms

Is data clustering in adversarial settings secure?
1. Mathematics of computing
  1. Probability and statistics
    1. Statistical paradigms
      1. Statistical graphics
2. Security and privacy
  1. Intrusion/anomaly detection and malware mitigation
  2. Systems security
    1. Operating systems security

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Information & Contributors

Information

Published In

cover image ACM Conferences

AISec '13: Proceedings of the 2013 ACM workshop on Artificial intelligence and security

November 2013

116 pages

ISBN:9781450324885

DOI:10.1145/2517312

General Chair:
Ahmad-Reza Sadeghi
TU Darmstadt, CASED, Intel ICRI-SC, Germany
,
Program Chairs:
Blaine Nelson
University of Potsdam, Germany
,
Christos Dimitrakakis
Chalmers University of Technology, Sweden
,
Elaine Shi
University of Maryland, College Park, MD, USA

Copyright © 2013 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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 November 2013

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CCS'13

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SIGSAC

CCS'13: 2013 ACM SIGSAC Conference on Computer and Communications Security

November 4, 2013

Berlin, Germany

Acceptance Rates

AISec '13 Paper Acceptance Rate 10 of 17 submissions, 59%;

Overall Acceptance Rate 94 of 231 submissions, 41%

Upcoming Conference

CCS '25

Sponsor:
sigsac

ACM SIGSAC Conference on Computer and Communications Security

October 13 - 17, 2025

Taipei , Taiwan

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Cited By

Zheng YLin CLyu XZhou XLi GWang T(2024)Robustness of Updatable Learning-based Index Advisors against Poisoning AttackProceedings of the ACM on Management of Data10.1145/36392652:1(1-26)Online publication date: 26-Mar-2024
https://dl.acm.org/doi/10.1145/3639265
Rass SKönig SAhmad SGoman M(2024)Metricizing the Euclidean Space Toward Desired Distance Relations in Point CloudsIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.342024619(7304-7319)Online publication date: 2024
https://doi.org/10.1109/TIFS.2024.3420246
Mongardini ALa Morgia MJajodia SVincenzo Mancini LMei A(2024)DARD: Deceptive Approaches for Robust Defense Against IP TheftIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.340243319(5591-5606)Online publication date: 2024
https://doi.org/10.1109/TIFS.2024.3402433
Omari RKim JMontague P(2024) Adversarial Robustness on Image Classification With k -Means IEEE Access10.1109/ACCESS.2024.336551712(28853-28859)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3365517
Yang RKornaropoulos ECheng Y(2023)Algorithmic Complexity Attacks on Dynamic Learned IndexesProceedings of the VLDB Endowment10.14778/3636218.363623217:4(780-793)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.14778/3636218.3636232
Xu YWei XDai PCao X(2023)A2SC: Adversarial Attacks on Subspace ClusteringACM Transactions on Multimedia Computing, Communications, and Applications10.1145/358709719:6(1-23)Online publication date: 12-Jul-2023
https://dl.acm.org/doi/10.1145/3587097
Liang JZhang XShang YGuo SLi C(2023)Clean-label Poisoning Attack against Fake News Detection Models2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386777(3614-3623)Online publication date: 15-Dec-2023
https://doi.org/10.1109/BigData59044.2023.10386777
Rios Insua DNaveiro RGallego VPoulos J(2023)Adversarial Machine Learning: Bayesian PerspectivesJournal of the American Statistical Association10.1080/01621459.2023.2183129118:543(2195-2206)Online publication date: 31-Mar-2023
https://doi.org/10.1080/01621459.2023.2183129
Katzir ZElovici Y(2023)Adversarial Machine LearningMachine Learning for Data Science Handbook10.1007/978-3-031-24628-9_25(559-585)Online publication date: 26-Feb-2023
https://doi.org/10.1007/978-3-031-24628-9_25
Chhabra ASekhari AMohapatra PKoyejo SMohamed SAgarwal ABelgrave DCho KOh A(2022)On the robustness of deep clustering modelsProceedings of the 36th International Conference on Neural Information Processing Systems10.5555/3600270.3601765(20566-20579)Online publication date: 28-Nov-2022
https://dl.acm.org/doi/10.5555/3600270.3601765
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