Social Learning Against Data Falsification in Sensor Networks

TL;DR

This paper introduces a social learning-based data aggregation scheme for sensor networks that enhances resilience against data falsification attacks, especially when critical nodes are compromised, and is suitable for resource-constrained devices.

Contribution

It proposes a novel social learning approach for secure data fusion in sensor networks, including a low-complexity algorithm for practical implementation.

Findings

Social learning improves network resilience against data falsification.

The scheme is effective even when many nodes are compromised.

A low-complexity algorithm enables deployment on resource-limited devices.

Abstract

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