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Although surveillance and sensor networks play a key role in Internet ofThings, sensor nodes are usually vulnerable to tampering due to theirwidespread locations. In this letter we consider data falsification attackswhere an smart attacker takes control of critical nodes within the network,including nodes serving as fusion centers. In order to face this criticalsecurity thread, we propose a data aggregation scheme based on social learning,resembling the way in which agents make decisions in social networks. Ourresults suggest that social learning enables network resilience, even when asignificant portion of the nodes have been compromised by the attacker.Finally, we show the suitability of our scheme to sensor networks by developinga low-complexity algorithm to facilitate the social learning data fusion rulein devices with restricted computational power.

Social Learning Against Data Falsification in Sensor Networks