Abstract
With a large number of sensors and control units in networked systems, distributed support vector machines (DSVMs) play a fundamental role in scalable and efficient multi-sensor classification and prediction tasks. However, DSVMs are vulnerable to adversaries who can modify and generate data to deceive the system to misclassification and misprediction. This work aims to design defense strategies for DSVM learner against a potential adversary. We use a game-theoretic framework to capture the conflicting interests between the DSVM learner and the attacker. The Nash equilibrium of the game allows predicting the outcome of learning algorithms in adversarial environments, and enhancing the resilience of the machine learning through dynamic distributed algorithms. We develop a secure and resilient DSVM algorithm with rejection method, and show its resiliency against adversary with numerical experiments.
| Original language | English (US) |
|---|---|
| Title of host publication | 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 4582-4587 |
| Number of pages | 6 |
| Volume | 2018-January |
| ISBN (Electronic) | 9781509028733 |
| DOIs | |
| State | Published - Jan 18 2018 |
| Event | 56th IEEE Annual Conference on Decision and Control, CDC 2017 - Melbourne, Australia Duration: Dec 12 2017 → Dec 15 2017 |
Other
| Other | 56th IEEE Annual Conference on Decision and Control, CDC 2017 |
|---|---|
| Country | Australia |
| City | Melbourne |
| Period | 12/12/17 → 12/15/17 |
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ASJC Scopus subject areas
- Decision Sciences (miscellaneous)
- Industrial and Manufacturing Engineering
- Control and Optimization
Cite this
A game-theoretic defense against data poisoning attacks in distributed support vector machines. / Zhang, Rui; Zhu, Quanyan.
2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017. Vol. 2018-January Institute of Electrical and Electronics Engineers Inc., 2018. p. 4582-4587.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - A game-theoretic defense against data poisoning attacks in distributed support vector machines
AU - Zhang, Rui
AU - Zhu, Quanyan
PY - 2018/1/18
Y1 - 2018/1/18
N2 - With a large number of sensors and control units in networked systems, distributed support vector machines (DSVMs) play a fundamental role in scalable and efficient multi-sensor classification and prediction tasks. However, DSVMs are vulnerable to adversaries who can modify and generate data to deceive the system to misclassification and misprediction. This work aims to design defense strategies for DSVM learner against a potential adversary. We use a game-theoretic framework to capture the conflicting interests between the DSVM learner and the attacker. The Nash equilibrium of the game allows predicting the outcome of learning algorithms in adversarial environments, and enhancing the resilience of the machine learning through dynamic distributed algorithms. We develop a secure and resilient DSVM algorithm with rejection method, and show its resiliency against adversary with numerical experiments.
AB - With a large number of sensors and control units in networked systems, distributed support vector machines (DSVMs) play a fundamental role in scalable and efficient multi-sensor classification and prediction tasks. However, DSVMs are vulnerable to adversaries who can modify and generate data to deceive the system to misclassification and misprediction. This work aims to design defense strategies for DSVM learner against a potential adversary. We use a game-theoretic framework to capture the conflicting interests between the DSVM learner and the attacker. The Nash equilibrium of the game allows predicting the outcome of learning algorithms in adversarial environments, and enhancing the resilience of the machine learning through dynamic distributed algorithms. We develop a secure and resilient DSVM algorithm with rejection method, and show its resiliency against adversary with numerical experiments.
UR - http://www.scopus.com/inward/record.url?scp=85037166233&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85037166233&partnerID=8YFLogxK
U2 - 10.1109/CDC.2017.8264336
DO - 10.1109/CDC.2017.8264336
M3 - Conference contribution
VL - 2018-January
SP - 4582
EP - 4587
BT - 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
ER -