This paper describes a distributed statistical estimation problem, corresponding to a network of agents. The network may be vulnerable to data injection attacks, in which attackers control legitimate nodes in the network and use them to inject false data. We have previously shown  that the detection metric by Wu et. al in , is vulnerable to sophisticated attacks where the attacker mixes normal behaviour and false data injection. In this paper we propose a novel metric that can be computed locally by each agent to detect and localize the novel attack in the network in a single instance.
|Original language||American English|
|Title of host publication||2019 IEEE Data Science Workshop, DSW 2019 - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||5|
|State||Published - Jun 2019|
|Event||2019 IEEE Data Science Workshop, DSW 2019 - Minneapolis, United States|
Duration: 2 Jun 2019 → 5 Jun 2019
|Name||2019 IEEE Data Science Workshop, DSW 2019 - Proceedings|
|Conference||2019 IEEE Data Science Workshop, DSW 2019|
|Period||2/06/19 → 5/06/19|
Bibliographical noteFunding Information:
This work is supported by the NSF CCF–BSF 1714672 and grants ISF– 1644/18 and ISF-NRF 2277/16.
© 2019 IEEE.
- Convex optimization
- Data injection attacks
- Decentralized optimization
- Distributed projected gradient