TY - GEN
T1 - Data injection attack on decentralized optimization
AU - Xiaoxiao Wu, Sissi
AU - Wai, Hoi To
AU - Scaglione, Anna
AU - Nedich, Angelia
AU - Leshem, Amira
N1 - Funding Information:
This work is supported by the National Natural Science Foundation of China under Grant 009989, the US National Science Foundation EAGER CCF 1553746, NSF CCF-BSF 1714672, and BSF Grant 2016660.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/9/10
Y1 - 2018/9/10
N2 - 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.
AB - 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.
KW - Data injection attack
KW - Decentralized optimization
KW - Gossip algorithms
UR - http://www.scopus.com/inward/record.url?scp=85054284797&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85054284797&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2018.8462528
DO - 10.1109/ICASSP.2018.8462528
M3 - Conference contribution
AN - SCOPUS:85054284797
SN - 9781538646588
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 3644
EP - 3648
BT - 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
Y2 - 15 April 2018 through 20 April 2018
ER -