Δημοσιεύσεις

@conference{9798011,

title = {A Hybrid RF-FSO Offloading Scheme for Autonomous Industrial Internet of Things},

author = { Dimitrios Pliatsios and Thomas Lagkas and Vasileios Argyriou and Antonios Sarigiannidis and Dimitrios Margounakis and Theocharis Saoulidis and Panagiotis Sarigiannidis},

doi = {10.1109/INFOCOMWKSHPS54753.2022.9798011},

isbn = {978-1-6654-0926-1},

year  = {2022},

date = {2022-01-01},

booktitle = {IEEE INFOCOM 2022 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)},

pages = {1-6},

abstract = {The ever increasing demand for bandwidth triggered by data-intensive applications is imposing a considerable burden on the radio-frequency (RF) spectrum. A promising solution to address the spectrum congestion problem is the adoption of free-space optical (FSO) communications. In this work, we consider a hybrid RF-FSO system that enables the task offloading process from Industrial Internet-of-Things devices to a multi-access edge computing (MEC)-enabled base station (BS). We propose a solution that minimizes the total energy consumption of the system by deciding whether the RF or FSO link will be used for the task offloading and optimally allocating the device transmission power while taking into account the task requirements in terms of delay. The proposed solution is based on a decomposition-driven algorithm that employs integer linear programming (ILP) and Lagrange dual decomposition. Finally, we carry out system-level Monte Carlo simulations to evaluate the performance of the solution. The simulation results show that the proposed solution can minimize the total energy consumption within a few iterations, while also considering the respective latency requirements.},

keywords = {Computation offloading, energy efficiency, Free-space Optical Communications, Industrial Internet of Things, Multi-access Edge Computing},

pubstate = {published},

tppubtype = {conference}

}

@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}

}