@conference{Siniosoglou2020,

title = {NeuralPot: An Industrial Honeypot Implementation Based On Deep Neural Networks},

author = { I. Siniosoglou and G. Efstathopoulos and D. Pliatsios and I.D. Moscholios and A. Sarigiannidis and G. Sakellari and G. Loukas and P. Sarigiannidis},

editor = { 2020 {IEEE} Symposium on Computers and Communications ({ISCC})},

url = {https://www.researchgate.net/publication/347267819_NeuralPot_An_Industrial_Honeypot_Implementation_Based_On_Deep_Neural_Networks},

doi = {10.1109/ISCC50000.2020.9219712},

year  = {2020},

date = {2020-07-01},

booktitle = {2020 IEEE Symposium on Computers and Communications (ISCC)},

journal = {Proceedings - IEEE Symposium on Computers and Communications},

publisher = {IEEE},

abstract = {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. © 2020 IEEE.},

keywords = {Autoencoder Network, Data Generation, GAN Network, Honeypots, Industrial Control System, SCADA},

pubstate = {published},

tppubtype = {conference}

}