NeuralPot: An Industrial Honeypot Implementation Based On Deep Neural Networks
Ilias Siniosoglou, Georgios Efstathopoulos, Dimitrios Pliatsios, Ioannis D. Moscholios, Antonios Sarigiannidis, Georgia Sakellari, Georgios Loukas, Panagiotis Sarigiannidis
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.