H2020 SPEAR Project
Published October 12, 2020 | Version v1
Conference paper Open

NeuralPot: An Industrial Honeypot Implementation Based On Deep Neural Networks

Creators

  • 1. Department of Electrical and Computer Engineering, University of Western Macedonia, Kozani, Greece
  • 2. 0 INFINITY Limited, Imperial Offices, London, United Kingdom
  • 3. Department of Informatics and Telecommunications, University of Peloponnese, Tripoli, Greece
  • 4. Sidroco Holdings Ltd, Limassol, Cyprus
  • 5. Computing and Information Systems, University of Greenwich, London, United Kingdom

Description

Honeypots are powerful security tools, developed to shield commercial and industrial networks from malicious activity. Honeypots act as passive and interactive decoys in a network attracting malicious activity and securing the rest of the network entities. Since an increase in intrusions has been observed lately, more advanced security systems are necessary. In this paper a new method of adapting a honeypot system in a modern industrial network, employing the Modbus protocol, is introduced. In the presented NeuralPot honeypot, two distinct deep neural network implementations are utilized to adapt to network Modbus entities and clone them, actively confusing the intruders. The proposed deep neural networks and their generated data are then compared.

Files

[19] NeuralPot An Industrial Honeypot Implementation Based On Deep Neural Networks.pdf

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Additional details

Funding

European Commission
SPEAR – SPEAR: Secure and PrivatE smArt gRid 787011