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- W4285141068 abstract "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) <inline-formula><tex-math notation=LaTeX>$e-Greedy$</tex-math></inline-formula> and (b) <inline-formula><tex-math notation=LaTeX>$Q-Learning$</tex-math></inline-formula>. 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." @default.
- W4285141068 created "2022-07-14" @default.
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- W4285141068 date "2022-01-01" @default.
- W4285141068 modified "2023-09-26" @default.
- W4285141068 title "Strategic Honeypot Deployment in Ultra-Dense Beyond 5G Networks: A Reinforcement Learning Approach" @default.
- W4285141068 doi "https://doi.org/10.1109/tetc.2022.3184112" @default.
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