Abstract
Gossip based optimization and learning are appealing methods that solve big data learning problems sharing computation and network resources when data are distributed. The main advantage these methods offer is that they are fault tolerant. Their flat architecture, however, expands the attack surface in the case of a data injection attack. We analyze the effects of data injection on the asymptotic behavior of the network and draw a parallel with the case of opinion dynamics in a network where zealots inject opinions to mislead a community. We further propose a possible decentralized detection of such attacks and analyze its performance.
Original language | English (US) |
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Title of host publication | Conference Record - Asilomar Conference on Signals, Systems and Computers |
Publisher | IEEE Computer Society |
Pages | 350-354 |
Number of pages | 5 |
Volume | 2016-February |
ISBN (Print) | 9781467385763 |
DOIs | |
State | Published - Feb 26 2016 |
Event | 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 - Pacific Grove, United States Duration: Nov 8 2015 → Nov 11 2015 |
Other
Other | 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 |
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Country/Territory | United States |
City | Pacific Grove |
Period | 11/8/15 → 11/11/15 |
Keywords
- attack detection
- data injection attack
- decentralized learning
- randomized gossip protocol
ASJC Scopus subject areas
- Computer Networks and Communications
- Signal Processing