Abstract
This paper studies the security aspect of gossip-based decentralized optimization algorithms for multi agent systems against data injection attacks. Our contributions are two-fold. First, we show that the popular distributed projected gradient method (by Nedić et al.) can be attacked by coordinated insider attacks, in which the attackers are able to steer the final state to a point of their choosing. Second, we propose a metric that can be computed locally by the trustworthy agents processing their own iterates and those of their neighboring agents. This metric can be used by the trustworthy agents to detect and localize the attackers. We conclude the paper by supporting our findings with numerical experiments.
| Original language | English |
|---|---|
| Title of host publication | 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 3644-3648 |
| Number of pages | 5 |
| ISBN (Print) | 9781538646588 |
| DOIs | |
| State | Published - 10 Sep 2018 |
| Externally published | Yes |
| Event | 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Calgary, Canada Duration: 15 Apr 2018 → 20 Apr 2018 |
Publication series
| Name | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings |
|---|---|
| Volume | 2018-April |
| ISSN (Print) | 1520-6149 |
Conference
| Conference | 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 |
|---|---|
| Country/Territory | Canada |
| City | Calgary |
| Period | 15/04/18 → 20/04/18 |
Bibliographical note
Publisher Copyright:© 2018 IEEE.
Keywords
- Data injection attack
- Decentralized optimization
- Gossip algorithms