@inproceedings{chochliouros2022fundamental,

title = {Fundamental Features of the Smart5Grid Platform Towards Realizing 5G Implementation},

author = {Ioannis P Chochliouros and Daniele Porcu and Sonia Castro and Borja Otura and Paula Encinar and Antonello Corsi and Irina Ciornei and Rita Santiago and Angelos Antonopoulos and Nicola Cadenelli and others},

url = {https://link.springer.com/chapter/10.1007/978-3-031-08341-9_12},

doi = {10.1007/978-3-031-08341-9_12},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

booktitle = {IFIP International Conference on Artificial Intelligence Applications and Innovations},

pages = {134--147},

organization = {Springer},

abstract = {Based on the original framework of the Smart5Grid EU-funded project, the present paper examines some fundamental features of the related platform that can be able to affect 5G implementation as well as the intended NetApps. Thus we examine: (i) the specific context of smart energy grids, enhanced by the inclusion of ICT and also supported by 5G connectivity; (ii) the cloud native context, together with the example of the cloud native VNF modelling, and; (iii) the MEC context as a 5G enabler for integrating management, control and orchestration processes. Each one is assessed compared to the state of the design and the implementation of the Smart5Grid platform. As a step further, we propose a preliminary framework for the definition of the NetApps, following to the way how the previous essential features are specifically incorporated within the project processes.},

keywords = {5G, Cloud computing, MEC, NetApp, Smart Grid, VNF},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{porcu20215g,

title = {5G Communications as “Enabler” for Smart Power Grids: The Case of the Smart5Grid Project},

author = {Daniele Porcu and Ioannis P Chochliouros and Sonia Castro and Giampaolo Fiorentino and Rui Costa and Dimitrios Nodaros and Vaios Koumaras and Fabrizio Brasca and Nicola di Pietro and George Papaioannou and others},

url = {https://link.springer.com/chapter/10.1007/978-3-030-79157-5_1},

doi = {10.1007/978-3-030-79157-5_1},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

booktitle = {IFIP International Conference on Artificial Intelligence Applications and Innovations},

pages = {7--20},

organization = {Springer},

abstract = {The fast 5G deployment at global level influences a variety of vertical sectors and offers many opportunities for growth and innovation, drastically affecting modern economies. Among the major sectors where significant benefits are expected is the case of smart grid, where the management of energy demand is expected to become more efficient, leading to less investments. 5G inclusion and adaptation in smart grid will allow easier balance of energy load and reduction of electricity peaks, together with savings of energy cost. In the present work we introduce the innovative scope proposed by the Smart5Grid research project, aiming to complement contemporary energy distribution grids with access to 5G network resources through an open experimentation 5G platform and innovative Network Applications (NetApps). Smart5Grid administers four meaningful use cases for the energy vertical ecosystem, in order to demonstrate efficiency, resilience and elasticity provided by the 5G networks. In particular, each one among these use cases is presented and assessed as of its expected benefits and proposed novelties, based on the corresponding demonstration actions.},

keywords = {5G, Energy management, NetApp, Smart Grid},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{sun2021intentional,

title = {Intentional Islanding of Power Systems Through Self-Embedding Learning},

author = {Zhonglin Sun and Yannis Spyridis and Achilleas Sessis and Georgios Efstathopoulos and Elisavet Grigoriou and Thomas Lagkas and Panagiotis Sarigiannidis},

url = {https://ieeexplore.ieee.org/abstract/document/9682069},

doi = {10.1109/GCWkshps52748.2021.9682069},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

booktitle = {2021 IEEE Globecom Workshops (GC Wkshps)},

pages = {1--6},

organization = {IEEE},

abstract = {Intentional islanding is a procedure to divide the electrical grid into several parts to guarantee the stability of a system in the case of failure. This study provides an unsupervised deep neural network to deal with the issue of intentional islanding. We propose to use a self-learning neural network to improve the generalisation performance of the islanding task. In addition, we use a merging technology to assign isolated buses to their neighbour's label. Experiments are carried out on several grid cases to illustrate the effect of our solution.},

keywords = {Deep Learning, Intentional islanding, Smart Grid},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1145/3407023.3409314,

title = {DIDEROT: An Intrusion Detection and Prevention System for DNP3-Based SCADA Systems},

author = {Panagiotis Radoglou-Grammatikis and Panagiotis Sarigiannidis and George Efstathopoulos and Paris-Alexandros Karypidis and Antonios Sarigiannidis},

url = {https://doi.org/10.1145/3407023.3409314},

doi = {10.1145/3407023.3409314},

isbn = {9781450388337},

year  = {2020},

date = {2020-01-01},

booktitle = {Proceedings of the 15th International Conference on Availability, Reliability and Security},

publisher = {Association for Computing Machinery},

address = {Virtual Event, Ireland},

series = {ARES '20},

abstract = {In this paper, an Intrusion Detection and Prevention System (IDPS) for the Distributed Network Protocol 3 (DNP3) Supervisory Control and Data Acquisition (SCADA) systems is presented. The proposed IDPS is called DIDEROT (Dnp3 Intrusion DetEction pReventiOn sysTem) and relies on both supervised Machine Learning (ML) and unsupervised/outlier ML detection models capable of discriminating whether a DNP3 network flow is related to a particular DNP3 cyberattack or anomaly. First, the supervised ML detection model is applied, trying to identify whether a DNP3 network flow is related to a specific DNP3 cyberattack. If the corresponding network flow is detected as normal, then the unsupervised/outlier ML anomaly detection model is activated, seeking to recognise the presence of a possible anomaly. Based on the DIDEROT detection results, the Software Defined Networking (SDN) technology is adopted in order to mitigate timely the corresponding DNP3 cyberattacks and anomalies. The performance of DIDEROT is demonstrated using real data originating from a substation environment.},

keywords = {Anomaly Detection, Autonencoder, Intrusion detection, Machine Learning, SCADA, SDN, Smart Grid},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{inproceedingsb,

title = {Secure and Private Smart Grid: The SPEAR Architecture},

author = {Panagiotis Radoglou Grammatikis and Panagiotis Sarigiannidis and Eider Iturbe and Erkuden Rios and Antonios Sarigiannidis and Odysseas Nikolis and Dimosthenis Ioannidis and Vasileios Machamint and Michalis Tzifas and Alkiviadis Giannakoulias and Michail Angelopoulos and Anastasios Papadopoulos and Francisco Ramos},

url = {https://www.researchgate.net/publication/343621502_Secure_and_Private_Smart_Grid_The_SPEAR_Architecture},

doi = {10.1109/NetSoft48620.2020.9165420},

year  = {2020},

date = {2020-01-01},

booktitle = {2020 6th IEEE International Conference on Network Softwarization (NetSoft)},

pages = {450-456},

abstract = {Information and Communication Technology (ICT) is an integral part of Critical Infrastructures (CIs), bringing both significant pros and cons. Focusing our attention on the energy sector, ICT converts the conventional electrical grid into a new paradigm called Smart Grid (SG), providing crucial benefits such as pervasive control, better utilisation of the existing resources, self-healing, etc. However, in parallel, ICT increases the attack surface of this domain, generating new potential cyberthreats. In this paper, we present the Secure and PrivatE smArt gRid (SPEAR) architecture which constitutes an overall solution aiming at protecting SG, by enhancing situational awareness, detecting timely cyberattacks, collecting appropriate forensic evidence and providing an anonymous cybersecurity information-sharing mechanism. Operational characteristics and technical specifications details are analysed for each component, while also the communication interfaces among them are described in detail.},

keywords = {Anomaly Detection, Anonymity, Cybersecurity, Forensics, Honeypots, Intrusion detection, Privacy, Smart Grid},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{8858503,

title = {Operational Data Based Intrusion Detection System for Smart Grid},

author = {Georgios Efstathopoulos and Panagiotis Radoglou Grammatikis and Panagiotis Sarigiannidis and Vasilis Argyriou and Antonios Sarigiannidis and Konstantinos Stamatakis and Michail K Angelopoulos and Solon K Athanasopoulos},

url = {https://ieeexplore.ieee.org/document/8858503},

doi = {10.1109/CAMAD.2019.8858503},

year  = {2019},

date = {2019-01-01},

booktitle = {2019 IEEE 24th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD)},

pages = {1-6},

abstract = {With the rapid progression of Information and Communication Technology (ICT) and especially of Internet of Things (IoT), the conventional electrical grid is transformed into a new intelligent paradigm, known as Smart Grid (SG). SG provides significant benefits both for utility companies and energy consumers such as the two-way communication (both electricity and information), distributed generation, remote monitoring, self-healing and pervasive control. However, at the same time, this dependence introduces new security challenges, since SG inherits the vulnerabilities of multiple heterogeneous, co-existing legacy and smart technologies, such as IoT and Industrial Control Systems (ICS). An effective countermeasure against the various cyberthreats in SG is the Intrusion Detection System (IDS), informing the operator timely about the possible cyberattacks and anomalies. In this paper, we provide an anomaly-based IDS especially designed for SG utilising operational data from a real power plant. In particular, many machine learning and deep learning models were deployed, introducing novel parameters and feature representations in a comparative study. The evaluation analysis demonstrated the efficacy of the proposed IDS and the improvement due to the suggested complex data representation.},

keywords = {Anomaly Detection, Cybersecurity, Intrusion Detection System, Machine Learning, Operational Data, Smart Grid},

pubstate = {published},

tppubtype = {inproceedings}

}