The subject of this talk is the detection and mitigation
of data injection attacks in randomized average consensus
gossip algorithms. Gossip algorithms fault tolerance
and distributed nature is unfortunately vulnerable to a data injection
attack. This form of attack, presented in the context of
sensor network security,  is identical to existing
models for opinion dynamics (the so called DeGroot model) with
stubborn agents steering the opinions of the group towards a
final state of their choosing.
We propose two novel strategies for detecting and
locating attackers, and study their detection and localization
performance numerically and analytically. Our detection and
localization methods are completely decentralized and, therefore
nodes can directly act on their conclusions and stop receiving
information from nodes identified as attackers. As we show
by simulation the network can often recover in this fashion,
leveraging, the intrinsic resilience of randomized gossiping to reduced
network connectivity.