@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}

}

@article{Boursianis2020,

title = {Smart Irrigation System for Precision Agriculture - The AREThOU5A IoT Platform},

author = { A. D. Boursianis and M. S. Papadopoulou and A. Gotsis and S. Wan and P. Sarigiannidis and S. Nikolaidis and S. K. Goudos},

url = {Smart Irrigation System for Precision Agriculture - The AREThOU5A IoT Platform},

doi = {10.1109/jsen.2020.3033526},

year  = {2020},

date = {2020-01-01},

journal = {IEEE Sensors Journal},

pages = {1--1},

publisher = {Institute of Electrical and Electronics Engineers (IEEE)},

abstract = {Agriculture 4.0, as the future of farming technology, includes several key enabling technologies towards sustainable agriculture. The use of state-of-the-art technologies, such as the Internet of Things, transform traditional cultivation practices, like irrigation, to modern solutions of precision agriculture. In this paper, we present in detail the subsystems and the architecture of an intelligent irrigation system for precision agriculture, the AREThOU5A IoT platform. We describe the operation of the IoT node that is utilized in the platform. Moreover, we apply the radiofrequency energy harvesting technique to the presented IoT platform, as an alternative technique to deliver power to the IoT node of the platform. To this end, we fabricate and validate a rectenna module for radiofrequency energy harvesting. Experimental results of the fabricated rectenna exhibit a satisfactory performance as a harvester of ambient sources in an outdoor environment. IEEE},

keywords = {Intelligent sensors, IoT technology, Irrigation, precision agriculture, Radio frequency, radio frequency energy harvesting, smart irrigation, Wireless communication, wireless sensor networks},

pubstate = {published},

tppubtype = {article}

}