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Social learning for resilient data fusion against data falsification attacks.

Fernando Rosas1,2, Kwang-Cheng Chen3, Deniz Gündüz2

  • 11Centre of Complexity Science and Department of Mathematics, Imperial College London, Kensington, London, SW72AZ UK.

Computational Social Networks
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Summary

This study introduces a novel approach for Internet of Things (IoT) security, using social learning principles in sensor networks to prevent data falsification attacks and enhance resilience against compromised nodes.

Keywords:
Byzantine nodesCollective behaviourData falsification attacksData fusionDistributed decision-makingInformation cascadesMulti-agent systemsSensor networksSocial learningSocial networks

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Area of Science:

  • Computer Science
  • Network Security
  • Artificial Intelligence

Background:

  • Internet of Things (IoT) networks are susceptible to data falsification attacks due to vulnerable sensor nodes.
  • Centralized systems in IoT create single points of failure exploitable by attackers.

Purpose of the Study:

  • To propose a novel scheme for distributed decision-making and data aggregation in IoT networks.
  • To enhance the security and robustness of IoT sensor networks against sophisticated attacks.

Main Methods:

  • Developed a scheme where sensor nodes operate based on social learning principles, similar to agents in a social network.
  • Implemented distributed decision-making and data aggregation across the entire network.

Main Results:

  • Analyzed conditions for local actions to propagate through the network, including the impact of Byzantine nodes.
  • Demonstrated high network performance even with a significant percentage of compromised nodes.

Conclusions:

  • Social learning principles are effective for building robust IoT sensor networks.
  • The proposed scheme enhances resilience against data falsification attacks in IoT environments.