Strategic Honeypot Deployment in Ultra-Dense Beyond 5G Networks: A Reinforcement Learning Approach

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Strategic Honeypot Deployment in Ultra-Dense Beyond 5G Networks: A Reinforcement Learning Approach

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  • June 1, 2022
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Panagiotis Radoglou Grammatikis, Panagiotis Sarigiannidis, Panagiotis Diamantoulakis, Thomas Lagkas, Theocharis Saoulidis, Eleftherios Fountoukidis, George Karagiannidis: Strategic Honeypot Deployment in Ultra-Dense Beyond 5G Networks: A Reinforcement Learning Approach. In: IEEE Transactions on Emerging Topics in Computing, 2022, ISSN: 2168-6750.

Abstract

The progression of Software Defined Networking (SDN) and the virtualisation technologies lead to the beyond 5G 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 ultradense 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.

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@article{articledb,
title = {Strategic Honeypot Deployment in Ultra-Dense Beyond 5G Networks: A Reinforcement Learning Approach},
author = {Panagiotis Radoglou Grammatikis and Panagiotis Sarigiannidis and Panagiotis Diamantoulakis and Thomas Lagkas and Theocharis Saoulidis and Eleftherios Fountoukidis and George Karagiannidis},
url = {https://www.researchgate.net/publication/361139812_Strategic_Honeypot_Deployment_in_Ultra-Dense_Beyond_5G_Networks_A_Reinforcement_Learning_Approach},
doi = {10.1109/TETC.2022.3184112},
issn = {2168-6750},
year  = {2022},
date = {2022-06-01},
urldate = {2022-01-01},
journal = {IEEE Transactions on Emerging Topics in Computing},
abstract = {The progression of Software Defined Networking (SDN) and the virtualisation technologies lead to the beyond 5G 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 ultradense 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.},
keywords = {Honeypot, Intrusion detection, ReinforcementLearning, Wireless communication},
pubstate = {published},
tppubtype = {article}
}
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