NeuPot: A Neural Network-Based Honeypot for Detecting Cyber Threats in Industrial Control Systems

Abstract

Honeypots have proven to be an effective defense method for industrial control systems (ICSs). However, as attacker skills become more sophisticated, it becomes increasingly difficult to develop honeypots that can effectively recognize and respond to such attacks. In this paper, we propose a neural network-based ICS honeypot scheme named Neupot that improves security from two aspects: honeypot interaction and cyber threats detection capability. Neupot can respond to attacker requests depending on a specific industrial scenario without constant communication with the ICS and detect malicious traffic. To create this honeypot scheme, a new seq2seq time-series forecast model guided by Huber loss is designed to simulate the long-term changes in actual ICS physical processes. Second, a Modbus honeypot framework is created to react to changes in these ICS physical processes in their interactions with attackers and to capture various cyber threats against the ICS. Further, a novel loss function for industrial protocol-level malicious traffic detection is devised to identify known and unknown threats. According to our experiments, the proposed honeypot scheme is highly effective and outperforms state-of-the-art schemes in terms of interactivity and in detecting cyber threats.