More Web Proxy on the site http://driver.im/

research-article

Poisoning attacks on cyber attack detectors for industrial control systems

Authors:

Moshe Kravchik,

Battista Biggio,

Asaf ShabtaiAuthors Info & Claims

SAC '21: Proceedings of the 36th Annual ACM Symposium on Applied Computing

Pages 116 - 125

https://doi.org/10.1145/3412841.3441892

Published: 22 April 2021 Publication History

Abstract

Recently, neural network (NN)-based methods, including autoencoders, have been proposed for the detection of cyber attacks targeting industrial control systems (ICSs). Such detectors are often retrained, using data collected during system operation, to cope with the natural evolution (i.e., concept drift) of the monitored signals. However, by exploiting this mechanism, an attacker can fake the signals provided by corrupted sensors at training time and poison the learning process of the detector such that cyber attacks go undetected at test time. With this research, we are the first to demonstrate such poisoning attacks on ICS cyber attack online NN detectors. We propose two distinct attack algorithms, namely, interpolation- and back-gradient based poisoning, and demonstrate their effectiveness on both synthetic and real-world ICS data. We also discuss and analyze some potential mitigation strategies.

References

[1]

Chuadhry Mujeeb Ahmed, Jianying Zhou, and Aditya P Mathur. 2018. Noise matters: Using sensor and process noise fingerprint to detect stealthy cyber attacks and authenticate sensors in cps. In Proc. 34th Annual Computer Security Applications Conference. 566--581.

Digital Library

[2]

Alessandro Erba, Riccardo Taormina, Stefano Galelli, Marcello Pogliani, Michele Carminati, Stefano Zanero, and Nils Ole Tippenhauer. 2019. Real-time evasion attacks with physical constraints on deep learning-based anomaly detectors in industrial control systems. arXiv preprint arXiv:1907.07487 (2019).

[3]

Cheng Feng, Tingting Li, Zhanxing Zhu, and Deeph Chana. 2017. A deep learning-based framework for conducting stealthy attacks in industrial control systems. arXiv preprint arXiv:1709.06397 (2017).

[4]

Amin Ghafouri, Yevgeniy Vorobeychik, and Xenofon Koutsoukos. 2018. Adversarial regression for detecting attacks in cyber-physical systems. In Proc. 27th Int'l Joint Conf. Artificial Intelligence. 3769--3775.

[5]

Jairo Giraldo, Esha Sarkar, Alvaro A Cardenas, Michail Maniatakos, and Murat Kantarcioglu. 2017. Security and privacy in cyber-physical systems: A survey of surveys. IEEE Design & Test 34, 4 (2017), 7--17.

[6]

Jairo Giraldo, David Urbina, Alvaro Cardenas, Junia Valente, Mustafa Faisal, Justin Ruths, Nils Ole Tippenhauer, Henrik Sandberg, and Richard Candell. 2018. A survey of physics-based attack detection in cyber-physical systems. ACM Computing Surveys (CSUR) 51, 4 (2018), 76.

Digital Library

[7]

Jonathan Goh, Sridhar Adepu, Khurum Nazir Junejo, and Aditya Mathur. 2016. A dataset to support research in the design of secure water treatment systems. In Int'l Conference on Critical Information Infrastructures Security. Springer, 88--99.

[8]

Jonathan Goh, Sridhar Adepu, Marcus Tan, and Zi Shan Lee. 2017. Anomaly detection in cyber physical systems using recurrent neural networks. In 18th Int'l Symp. High Assurance Systems Engineering (HASE). IEEE, 140--145.

[9]

Ian Goodfellow, Yoshua Bengio, Aaron Courville, and Yoshua Bengio. 2016. Deep learning. Vol. 1. MIT press Cambridge.

Digital Library

[10]

Amir Herzberg and Yehonatan Kfir. 2019. The chatty-sensor: a provably-covert channel in cyber physical systems. In Proc. 35th Annual Computer Security Applications Conference. 638--649.

Digital Library

[11]

Abdulmalik Humayed, Jingqiang Lin, Fengjun Li, and Bo Luo. 2017. Cyber-physical systems security --- A survey. IEEE Internet of Things Journal 4, 6 (2017), 1802--1831.

[12]

Jun Inoue, Yoriyuki Yamagata, Yuqi Chen, Christopher M Poskitt, and Jun Sun. 2017. Anomaly detection for a water treatment system using unsupervised machine learning. In Int'l Conf. Data Mining Workshops. IEEE, 1058--1065.

[13]

Jonguk Kim, Jeong-Han Yun, and Hyoung Chun Kim. 2019. Anomaly detection for industrial control systems using sequence-to-sequence neural networks. In Computer Security. Springer, 3--18.

[14]

Moshe Kravchik and Asaf Shabtai. 2018. Detecting cyber attacks in industrial control systems using convolutional neural networks. In Proc. 2018 Workshop on Cyber-Physical Systems Security and PrivaCy. ACM, 72--83.

Digital Library

[15]

Moshe Kravchik and Asaf Shabtai. 2019. Efficient cyber attacks detection in industrial control systems using lightweight neural networks. arXiv preprint arXiv:1907.01216 (2019).

[16]

Qin Lin, Sridha Adepu, Sicco Verwer, and Aditya Mathur. 2018. TABOR: a graphical model-based approach for anomaly detection in industrial control systems. In Proc. 2018 on Asia Conf. Comp. Comm. Sec. ACM, 525--536.

Digital Library

[17]

Dougal Maclaurin, David Duvenaud, and Ryan Adams. 2015. Gradient-based hyperparameter optimization through reversible learning. In Int'l Conference on Machine Learning. 2113--2122.

[18]

Pooria Madani and Natalija Vlajic. 2018. Robustness of deep autoencoder in intrusion detection under adversarial contamination. In Proc. 5th Annual Symp. and Bootcamp on Hot Topics in the Science of Security. ACM, 1.

Digital Library

[19]

Robert Mitchell and Ing-Ray Chen. 2014. A survey of intrusion detection techniques for cyber-physical systems. Comput. Surveys 46, 4 (2014), 55.

Digital Library

[20]

Luis Muñoz-González, Battista Biggio, Ambra Demontis, Andrea Paudice, Vasin Wongrassamee, Emil C Lupu, and Fabio Roli. 2017. Towards poisoning of deep learning algorithms with back-gradient optimization. In Proc. 10th ACM Workshop on Artificial Intelligence and Security. ACM, 27--38.

Digital Library

[21]

Mykola Pechenizkiy, Jorn Bakker, I Žliobaitė, Andriy Ivannikov, and Tommi Kärkkäinen. 2010. Online mass flow prediction in CFB boilers with explicit detection of sudden concept drift. ACM SIGKDD Explorations Newsletter 11, 2 (2010), 109--116.

Digital Library

[22]

Waldir Ribeiro Pires, Thiago H de Paula Figueiredo, Hao Chi Wong, and Antonio Alfredo Ferreira Loureiro. 2004. Malicious node detection in wireless sensor networks. In 18th Int'l Parallel and Distributed Processing Symp. IEEE, 24.

[23]

Gelli Ravikumar, Burhan Hyder, and Manimaran Govindarasu. 2020. Next-Generation CPS Testbed-based Grid Exercise-Synthetic Grid, Attack, and Defense Modeling. In 2020 Resilience Week (RWS). IEEE, 92--98.

[24]

Ishai Rosenberg, Asaf Shabtai, Lior Rokach, and Yuval Elovici. 2018. Generic black-box end-to-end attack against state of the art API call based malware classifiers. In Int'l Symp. Res. Attacks, Intrusions, and Defenses. Springer, 490--510.

[25]

Benjamin IP Rubinstein, Blaine Nelson, Ling Huang, Anthony D Joseph, Shinghon Lau, Satish Rao, Nina Taft, and J Doug Tygar. 2009. Antidote: understanding and defending against poisoning of anomaly detectors. In Proc. 9th ACM SIGCOMM conference on Internet measurement. 1--14.

Digital Library

[26]

Ali Shafahi, W Ronny Huang, Mahyar Najibi, Octavian Suciu, Christoph Studer, Tudor Dumitras, and Tom Goldstein. 2018. Poison frogs! targeted clean-label poisoning attacks on neural networks. In Adv. Neural Inf. Proc. Sys. 6103--6113.

[27]

Elaine Shi and Adrian Perrig. 2004. Designing secure sensor networks. IEEE Wireless Communications 11, 6 (2004), 38--43.

Digital Library

[28]

Octavian Suciu, Radu Marginean, Yigitcan Kaya, Hal Daume III, and Tudor Dumitras. 2018. When does machine learning FAIL? generalized transferability for evasion and poisoning attacks. In 27th USENIX Sec. Symp. 1299--1316.

[29]

Riccardo Taormina and Stefano Galelli. 2018. Deep-learning approach to the detection and localization of cyber-physical attacks on water distribution systems. Journal of Water Resources Planning and Management 144, 10 (2018), 04018065.

[30]

Riccardo Taormina, Stefano Galelli, Nils Ole Tippenhauer, Elad Salomons, Avi Ostfeld, Demetrios G Eliades, Mohsen Aghashahi, Raanju Sundararajan, Mohsen Pourahmadi, M Katherine Banks, et al. 2018. Battle of the attack detection algorithms: Disclosing cyber attacks on water distribution networks. Journal of Water Resources Planning and Management 144, 8 (2018), 04018048.

[31]

Xiaojun Zhou, Zhen Xu, Liming Wang, Kai Chen, Cong Chen, and Wei Zhang. 2018. APT attack analysis in SCADA systems. In MATEC Web of Conferences, Vol. 173. EDP Sciences, 01010.

[32]

Giulio Zizzo, Chris Hankin, Sergio Maffeis, and Kevin Jones. 2019. Adversarial machine learning beyond the image domain. In 2019 56th ACM/IEEE Design Automation Conference (DAC). IEEE, 1--4.

Digital Library

[33]

Giulio Zizzo, Chris Hankin, Sergio Maffeis, and Kevin Jones. 2019. Intrusion Detection for Industrial Control Systems: Evaluation Analysis and Adversarial Attacks. arXiv preprint arXiv:1911.04278 (2019).

Cited By

Sobien DKulkarni ABatarseh F(2025)Toward Identifying Cyber Dependencies in Water Distribution Systems Using Causal AIJournal of Water Resources Planning and Management10.1061/JWRMD5.WRENG-6488151:2Online publication date: Feb-2025
https://doi.org/10.1061/JWRMD5.WRENG-6488
AlOtaibi NFelemban MMahmood S(2024)Edge-Assisted Label-Flipping Attack Detection in Federated LearningIEEE Open Journal of the Communications Society10.1109/OJCOMS.2024.34968725(7278-7300)Online publication date: 2024
https://doi.org/10.1109/OJCOMS.2024.3496872
Bhattacharjee ABai GTushar WVerma AMishra SSaha T(2024)DeeBBAA: A Benchmark Deep Black-Box Adversarial Attack Against Cyber–Physical Power SystemsIEEE Internet of Things Journal10.1109/JIOT.2024.345425711:24(40670-40688)Online publication date: 15-Dec-2024
https://doi.org/10.1109/JIOT.2024.3454257
Show More Cited By

Poisoning attacks on cyber attack detectors for industrial control systems
1. Security and privacy

Recommendations

Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain

In recent years, machine learning algorithms, and more specifically deep learning algorithms, have been widely used in many fields, including cyber security. However, machine learning systems are vulnerable to adversarial attacks, and this limits the ...
Practical Evaluation of Poisoning Attacks on Online Anomaly Detectors in Industrial Control Systems
Abstract
Recently, neural networks (NNs) have been proposed for the detection of cyber attacks targeting industrial control systems (ICSs). Such detectors are often retrained, using data collected during system operation, to cope with the ...
Poisoning Attacks to Graph-Based Recommender Systems
ACSAC '18: Proceedings of the 34th Annual Computer Security Applications Conference

Recommender system is an important component of many web services to help users locate items that match their interests. Several studies showed that recommender systems are vulnerable to poisoning attacks, in which an attacker injects fake data to a ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

SAC '21: Proceedings of the 36th Annual ACM Symposium on Applied Computing

March 2021

2075 pages

ISBN:9781450381048

DOI:10.1145/3412841

Conference Chairs:
Chih-Cheng Hung
Kennesaw State University
,
Jiman Hong
Soongsil University, South Korea
,
Program Chairs:
Alessio Bechini
University of Pisa, Italy
,
Eunjee Song
Baylor University

Copyright © 2021 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]

Sponsors

SIGAPP: ACM Special Interest Group on Applied Computing

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 April 2021

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Horizon 2020 research and innovation programme

Conference

SAC '21

Sponsor:

SIGAPP

SAC '21: The 36th ACM/SIGAPP Symposium on Applied Computing

March 22 - 26, 2021

Virtual Event, Republic of Korea

Acceptance Rates

Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

Upcoming Conference

SAC '25

Sponsor:
sigapp

The 40th ACM/SIGAPP Symposium on Applied Computing

March 31 - April 4, 2025

Catania , Italy

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

20
Total Citations
View Citations
475
Total Downloads

Downloads (Last 12 months)96
Downloads (Last 6 weeks)10

Reflects downloads up to 19 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Sobien DKulkarni ABatarseh F(2025)Toward Identifying Cyber Dependencies in Water Distribution Systems Using Causal AIJournal of Water Resources Planning and Management10.1061/JWRMD5.WRENG-6488151:2Online publication date: Feb-2025
https://doi.org/10.1061/JWRMD5.WRENG-6488
AlOtaibi NFelemban MMahmood S(2024)Edge-Assisted Label-Flipping Attack Detection in Federated LearningIEEE Open Journal of the Communications Society10.1109/OJCOMS.2024.34968725(7278-7300)Online publication date: 2024
https://doi.org/10.1109/OJCOMS.2024.3496872
Bhattacharjee ABai GTushar WVerma AMishra SSaha T(2024)DeeBBAA: A Benchmark Deep Black-Box Adversarial Attack Against Cyber–Physical Power SystemsIEEE Internet of Things Journal10.1109/JIOT.2024.345425711:24(40670-40688)Online publication date: 15-Dec-2024
https://doi.org/10.1109/JIOT.2024.3454257
Chao DXu DGao FZhang CZhang WZhu L(2024)A Systematic Survey on Security in Anonymity Networks: Vulnerabilities, Attacks, Defenses, and FormalizationIEEE Communications Surveys & Tutorials10.1109/COMST.2024.335000626:3(1775-1829)Online publication date: Nov-2025
https://doi.org/10.1109/COMST.2024.3350006
Habler EBitton RAvraham DKlevansky EMimran DBrodt OLehmann HElovici YShabtai A(2024)Adversarial machine learning threat analysis and remediation in Open Radio Access Network (O-RAN)Journal of Network and Computer Applications10.1016/j.jnca.2024.104090(104090)Online publication date: Dec-2024
https://doi.org/10.1016/j.jnca.2024.104090
Canonico RSperlì G(2024)Industrial cyber-physical systems protectionComputers and Security10.1016/j.cose.2023.103531135:COnline publication date: 10-Jan-2024
https://dl.acm.org/doi/10.1016/j.cose.2023.103531
Gulzar QMustafa K(2024)An analytical survey of cyber‐physical systems in water treatment and distributionSecurity and Privacy10.1002/spy2.4407:6Online publication date: 4-Jul-2024
Goel S(2024)A systematic literature review on past attack analysis on industrial control systemsTransactions on Emerging Telecommunications Technologies10.1002/ett.500435:6Online publication date: 31-May-2024
https://dl.acm.org/doi/10.1002/ett.5004
Bitton RMaman NSingh IMomiyama SElovici YShabtai A(2023)Evaluating the Cybersecurity Risk of Real-world, Machine Learning Production SystemsACM Computing Surveys10.1145/355910455:9(1-36)Online publication date: 16-Jan-2023
https://dl.acm.org/doi/10.1145/3559104
Buja AApostolova MLuma A(2023)Enhancing Cyber Security in Industrial Internet of Things Systems: An Experimental Assessment2023 12th Mediterranean Conference on Embedded Computing (MECO)10.1109/MECO58584.2023.10155100(1-5)Online publication date: 6-Jun-2023
https://doi.org/10.1109/MECO58584.2023.10155100
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents