@conference{Radoglou-Grammatikis2021485,

title = {TRUSTY: A solution for threat hunting using data analysis in critical infrastructures},

author = { P. Radoglou-Grammatikis and A. Liatifis and E. Grigoriou and T. Saoulidis and A. Sarigiannidis and T. Lagkas and P. Sarigiannidis},

url = {https://www.researchgate.net/publication/354396254_TRUSTY_A_Solution_for_Threat_Hunting_Using_Data_Analysis_in_Critical_Infrastructures},

doi = {10.1109/CSR51186.2021.9527936},

year  = {2021},

date = {2021-01-01},

journal = {Proceedings of the 2021 IEEE International Conference on Cyber Security and Resilience, CSR 2021},

pages = {485-490},

abstract = {The rise of the Industrial Internet of Things (IIoT) plays a crucial role in the era of hyper-connected digital economies. Despite the valuable benefits, such as increased resiliency, self-monitoring and pervasive control, IIoT raises severe cybersecurity and privacy risks, allowing cyberattackers to exploit a plethora of vulnerabilities and weaknesses that can lead to disastrous consequences. Although the Intrusion Detection and Prevention Systems (IDPS) constitute valuable solutions, they suffer from several gaps, such as zero-day attacks, unknown anomalies and false positives. Therefore, the presence of supporting mechanisms is necessary. To this end, honeypots can protect the real assets and trap the cyberattackers. In this paper, we provide a web-based platform called TRUSTY , which is capable of aggregating, storing and analysing the detection results of multiple industrial honeypots related to Modbus/Transmission Control Protocol (TCP), IEC 60870-5-104, BACnet, Message Queuing Telemetry Transport (MQTT) and EtherNet/IP. Based on this analysis, we provide a dataset related to honeypot security events. Moreover, this paper provides a Reinforcement Learning (RL) method, which decides about the number of honeypots that can be deployed in an industrial environment in a strategic way. In particular, this decision is converted into a Multi-Armed Bandit (MAB), which is solved with the Thompson Sampling (TS) method. The evaluation analysis demonstrates the efficiency of the proposed method. © 2021 IEEE.},

keywords = {Cybersecurity, Dataset, Honeypot, Industrial Internet of Things, Multi-Armed Bandit, Reinforcement Learning, Thompson Sampling},

pubstate = {published},

tppubtype = {conference}

}

@conference{Kakalou20155608,

title = {A Reinforcement learning-based cognitive MAC protocol},

author = { I. Kakalou and G.I. Papadimitriou and P. Nicopolitidis and P.G. Sarigiannidis and M.S. Obaidat},

url = {https://www.researchgate.net/publication/308872233_A_Reinforcement_learning-based_cognitive_MAC_protocol},

doi = {10.1109/ICC.2015.7249216},

year  = {2015},

date = {2015-01-01},

journal = {IEEE International Conference on Communications},

volume = {2015-September},

pages = {5608-5613},

abstract = {A Multi-Channel Cognitive MAC Protocol for adhoc cognitive networks that uses a distributed learning reinforcement scheme is proposed in this paper. The proposed protocol learns the Primary User (PU) traffic characteristics and then selects the best channel to transmit. The scheme, whichaddresses overlay cognitive networks,avoids collision with the PU nodes and manages toexceed the performance of the less adaptive statistical channel selection schemesin normal and especially bursty traffic environments. The simulation analysis results have shown that the performance of our proposed scheme outperforms that of the CREAM-MAC scheme. © 2015 IEEE.},

keywords = {ad-hoc, Cognitive, MAC, next generation networks, Reinforcement Learning},

pubstate = {published},

tppubtype = {conference}

}