Strategic Honeypot Deployment in Ultra-Dense Beyond 5G Networks: A Reinforcement Learning Approach
Panagiotis I. Radoglou Grammatikis, Panagiotis G. Sarigiannidis, Panagiotis D. Diamantoulakis, Θωμάς Λάγκας, Theocharis Saoulidis, Eleftherios Fountoukidis, George K. Karagiannidis · IEEE Transactions on Emerging Topics in Computing · 2022
The progression of Software Defined Networking (SDN) and the virtualisation technologies lead to the beyond 5 G era, providing multiple benefits in the smart economies. However, despite the advantages, security issues still remain. In particular, SDN/NFV and cloud/edge computing are related to various security issues. Moreover, due to the wireless nature of the entities, they are prone to a wide range of cyberthreats. Therefore, the presence of appropriate intrusion detection mechanisms is critical. Although both Machine Learning (ML) and Deep Learning (DL) have optimised the typical rule-based detection systems, the use of ML and DL requires labelled pre-existing datasets. However, this kind of data varies based on the nature of the respective environment. Another smart solution for detecting intrusions is to use honeypots. A honeypot acts as a decoy with the goal to mislead the cyberatatcker and protect the real assets. In this paper, we focus on Wireless Honeypots (WHs) in ultra-dense networks. In particular, we introduce a strategic honeypot deployment method, using two Reinforcement Learning (RL) techniques: (a)$e-Greedy$and (b)$Q-Learning$. Both methods aim to identify the optimal number of honeypots that can be deployed for protecting the actual entities. The experimental results demonstrate the efficacy of both methods.