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NeuralPot: an industrial honeypot implementation based on convolutional neural networks

NeuralPot: an industrial honeypot implementation based on convolutional neural networks

Siniosoglou, Ilias, Efstathopoulos, Georgios, Pliatsios, Dimitrios, Moscholios, Ioannis, Sarigiannidis, Antonios, Sakellari, Georgia ORCID: 0000-0001-7238-8700, Loukas, George and Sarigiannidis, Panagiotis (2020) NeuralPot: an industrial honeypot implementation based on convolutional neural networks. In: 2020 IEEE Symposium on Computers and Communications (ISCC). IEEE. (In Press)

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Abstract

Honeypots are powerful security tools, which are developed to shield commercial and industrial networks from malicious activity. Honeypots act as passive and interactive decoys in a network by attracting malicious activity away from critical network devices. Given that the security incidents against industrial and critical infrastructure are getting sophisticated and persistent, advanced security systems are needed. In this paper, a novel industrial honeypot implementation is presented, which is based on the Modbus protocol, entitled NeuralPot. The presented NeuralPot honeypot is able to emulate industrial Modbus entities in order to actively confuse the intruders. It achieves this by introducing two distinct deep neural networks, a Generative Adversarial Network and an Autoencoder Network, which learn Modbus device behavior and generate realistic-looking traffic behavior. Based on the evaluation results, the proposed industrial honeypot performs well in terms of accuracy, similarity, and elapsed time of data generation.

Item Type: Conference Proceedings
Title of Proceedings: 2020 IEEE Symposium on Computers and Communications (ISCC)
Uncontrolled Keywords: industrial control system, SCADA, honeypots, GAN network, autoencoder network, data generation
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / Department / Research Group: Faculty of Liberal Arts & Sciences
Faculty of Liberal Arts & Sciences > Internet of Things and Security (ISEC)
Faculty of Liberal Arts & Sciences > School of Computing and Mathematical Sciences
Related URLs:
Last Modified: 30 Jun 2020 08:56
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: REF 3
URI: http://gala.gre.ac.uk/id/eprint/27976

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