@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{grigoriou2022towards,

title = {Towards the RESPOND-A initiative: Next-generation equipment tools and mission-critical strategies for First Responders},

author = {Elisavet Grigoriou and Manolis Fountoulakis and Emmanouil Kafetzakis and Ioannis Giannoulakis and Eleftherios Fountoukidis and Paris Alexandros Karypidis and Dimitrios Margounakis and Cleo Varianou Mikelidou and Iasonas Sennekis and George Boustras},

year  = {2022},

date = {2022-01-01},

booktitle = {2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS)},

pages = {1--5},

organization = {IEEE Computer Society},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{pliatsios2021dynamic,

title = {A Dynamic Recommendation-based Trust Scheme for the Smart Grid},

author = {Dimitrios Pliatsios and Panagiotis Sarigiannidis and George Fragulis and Apostolos Tsiakalos and Dimitrios Margounakis},

url = {https://zenodo.org/record/5565464},

doi = {10.1109/NetSoft51509.2021.9492542},

year  = {2021},

date = {2021-07-26},

urldate = {2021-01-01},

booktitle = {2021 IEEE 7th International Conference on Network Softwarization (NetSoft)},

pages = {464--469},

organization = {IEEE},

abstract = {The integration of the internet of things (IoT) concept into the traditional electricity grid introduces several critical vulnerabilities. Intrusion detection systems (IDSs) can be effective countermeasures against cyberattacks, however, they require considerable computational and storage resources. As IoT-enabled metering devices have limited resources, IDSs cannot efficiently ensure security. To this end, trust evaluation schemes have emerged as promising solutions toward protecting resource-constrained metering devices. In this work, we proposed a trust evaluation scheme for the smart grid, that is based on direct trust evaluation and recommendation. The proposed hierarchical scheme is able to evaluate the trustiness of each metering device without requiring any significant modifications to the already deployed infrastructure. Additionally, the proposed scheme features is dynamic, meaning that it is robust against non-adversarial events that negatively impact the device’s trustiness. To validate the performance of the proposed scheme, we carry out network-level simulations and investigate how the various network parameters impact the trust evaluation performance.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{inproceedings,

title = {A Self-Learning Approach for Detecting Intrusions in Healthcare Systems},

author = {Panagiotis Radoglou Grammatikis and Panagiotis Sarigiannidis and George Efstathopoulos and Thomas Lagkas and George Fragulis and Antonios Sarigiannidis},

url = {https://www.researchgate.net/publication/349158703_A_Self-Learning_Approach_for_Detecting_Intrusions_in_Healthcare_Systems/references},

doi = {10.1109/ICC42927.2021.9500354},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

booktitle = {IEEE International Conference on Communications},

abstract = {The rapid evolution of the Internet of Medical Things (IoMT) introduces the healthcare ecosystem into a new reality consisting of smart medical devices and applications that provide multiple benefits, such as remote medical assistance, timely administration of medication, real-time monitoring, preventive care and health education. However, despite the valuable advantages, this new reality increases the cybersecurity and privacy concerns since vulnerable IoMT devices can access and handle autonomously patients’ data. Furthermore, the continuous evolution of cyberattacks, malware and zero-day vulnerabilities require the development of the appropriate countermeasures. In the light of the aforementioned remarks, in this paper, we present an Intrusion Detection and Prevention System (IDPS), which can protect the healthcare communications that rely on the Hypertext Transfer Protocol (HTTP) and the Modbus/Transmission Control Protocol (TCP). HTTP is commonly adopted by conventional ICT healthcare-related services, such as web-based Electronic Health Record (EHR) applications, while Modbus/TCP is an industrial protocol adopted by IoMT. Although the Machine Learning (ML) and Deep Learning (DL) methods have already demonstrated their efficacy in detecting intrusions, the rarely available intrusion detection datasets (especially in the healthcare sector) complicate their global application. The main contribution of this work lies in the fact that an active learning approach is modelled and adopted in order to re-train dynamically the supervised classifiers behind the proposed IDPS. The evaluation analysis demonstrates the efficiency of this work against HTTP and Modbus/TCP cyberattacks, showing also how the entire accuracy is increased in the various re-training phases.},

keywords = {Cybersecurity, Healthcare, Machine Learning},

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{siniosoglou2021covert,

title = {Covert Distributed Training of Deep Federated Industrial Honeypots},

author = {Ilias Siniosoglou and Vasileios Argyriou and Thomas Lagkas and Apostolos Tsiakalos and Antonios Sarigiannidis and Panagiotis Sarigiannidis},

url = {https://ieeexplore.ieee.org/abstract/document/9682162},

doi = {10.1109/GCWkshps52748.2021.9682162},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

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

pages = {1--6},

organization = {IEEE},

abstract = {Since the introduction of automation technologies in the Industrial field and its subsequent scaling to horizontal and vertical extents, the need for interconnected industrial systems, supporting smart interoperability is ever higher. Due to this scaling, new and critical vulnerabilities have been created, notably in legacy systems, leaving Industrial infrastructures prone to cyber attacks, that can some times have catastrophic results. To tackle the need for extended security measures, this paper presents a Federated Industrial Honeypot that takes advantage of decentralized private Deep Training to produce models that accumulate and simulate real industrial devices. To enhance their camouflage, SCENT, a new custom and covert protocol is proposed, to fully immerse the Federated Honeypot to its industrial role, that handles the communication between the server and honeypot during the training, to hide any clues of operation of the honeypot other that its supposed objective to the eye of the attacker.},

keywords = {Data Generation, Deep Learning, Honeypots, Industrial Control System, SCADA},

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{inproceedingsb,

title = {NeuralPot: An Industrial Honeypot Implementation Based On Deep Neural Networks},

author = {Ilias Siniosoglou and Georgios Efstathopoulos and Dimitrios Pliatsios and Ioannis Moscholios and Antonios Sarigiannidis and Georgia Sakellari and Georgios Loukas and Panagiotis Sarigiannidis},

url = {https://www.researchgate.net/publication/347267819_NeuralPot_An_Industrial_Honeypot_Implementation_Based_On_Deep_Neural_Networks},

doi = {10.1109/ISCC50000.2020.9219712},

year  = {2020},

date = {2020-10-12},

urldate = {2020-01-01},

booktitle = {2020 IEEE Symposium on Computers and Communications (ISCC)},

pages = {1-7},

abstract = {Honeypots are powerful security tools, developed to shield commercial and industrial networks from malicious activity. Honeypots act as passive and interactive decoys in a network attracting malicious activity and securing the rest of the network entities. Since an increase in intrusions has been observed lately, more advanced security systems are necessary. In this paper a new method of adapting a honeypot system in a modern industrial network, employing the Modbus protocol, is introduced. In the presented NeuralPot honeypot, two distinct deep neural network implementations are utilized to adapt to network Modbus entities and clone them, actively confusing the intruders. The proposed deep neural networks and their generated data are then compared.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{inproceedingsb,

title = {A Novel LoRaWAN Scheduling Scheme for Improving Reliability and Collision Avoidance},

author = {Anna Triantafyllou and Panagiotis Sarigiannidis and Thomas Lagkas and Antonios Sarigiannidis},

url = {https://www.researchgate.net/publication/345185837_A_Novel_LoRaWAN_Scheduling_Scheme_for_Improving_Reliability_and_Collision_Avoidance},

doi = {10.1109/MOCAST49295.2020.9200253},

year  = {2020},

date = {2020-09-18},

urldate = {2020-01-01},

booktitle = {2020 9th International Conference on Modern Circuits and Systems Technologies (MOCAST)},

pages = {1-4},

abstract = {The employment of Low-Power Wide Area Networks (LPWANs) has proven quite beneficial to the advancement of the Internet of Things (IoT) paradigm. The utilization of low power but long range communication links of the LoRaWAN technology promises low energy consumption, while ensuring sufficient throughput. However, due to LoRa's original scheduling process there is a high chance of packet collisions, compromising the technology's reliability. In this paper, we propose a new Medium Access Control (MAC) protocol, entitled the RCA-LoRa towards improving reliability and collision avoidance in LoRa wide-area networks. The novel scheduling process that is introduced is based on the broadcasting of beacon frames by the network's gateway in order to synchronize communication with end devices. Our results demonstrate the benefits of RCA-LoRa over an enhanced version of the legacy LoRaWAN employing the ALOHA protocol and an advanced adaptive rate mechanism, in terms of throughput and collision avoidance. Indicatively, in a single cell scenario with 600 nodes, RCA-LoRa can increase throughput by nearly 50.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{inproceedingsb,

title = {Data Visualization in Internet of Things: Tools, Methodologies, and Challenges},

author = {Antonis Protopsaltis and Panagiotis Sarigiannidis and Dimitrios Margounakis and Anastasios Lytos},

url = {https://www.researchgate.net/publication/343935293_Data_Visualization_in_Internet_of_Things_Tools_Methodologies_and_Challenges},

doi = {10.1145/3407023.3409228},

year  = {2020},

date = {2020-01-01},

booktitle = {ARES 2020: The 15th International Conference on Availability, Reliability and Security},

abstract = {As the Internet of Things (IoT) grows rapidly, huge amounts of wireless sensor networks emerged monitoring a wide range of infrastructure, in various domains such as healthcare, energy, transportation, smart city, building automation, agriculture, and industry producing continuously streamlines of data. Big Data technologies play a significant role within IoT processes, as visual analytics tools, generating valuable knowledge in real-time in order to support critical decision making. This paper provides a comprehensive survey of visualization methods, tools, and techniques for the IoT. We position data visualization inside the visual analytics process by reviewing the visual analytics pipeline. We provide a study of various chart types available for data visualization and analyze rules for employing each one of them, taking into account the special conditions of the particular use case. We further examine some of the most promising visualization tools. Since each IoT domain is isolated in terms of Big Data approaches, we investigate visualization issues in each domain. Additionally, we review visualization methods oriented to anomaly detection. Finally, we provide an overview of the major challenges in IoT visualizations.},

keywords = {Anomaly Detection, Big Data, Data Visualization, Internet of Things (IoT)},

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 = {An Anomaly Detection Mechanism for IEC 60870-5-104},

author = {Panagiotis Radoglou Grammatikis and Panagiotis Sarigiannidis and Antonios Sarigiannidis and Dimitrios Margounakis and Apostolos Tsiakalos and Georgios Efstathopoulos},

url = {https://www.researchgate.net/publication/344386495_An_Anomaly_Detection_Mechanism_for_IEC_60870-5-104},

doi = {10.1109/MOCAST49295.2020.9200285},

year  = {2020},

date = {2020-01-01},

booktitle = {2020 9th International Conference on Modern Circuits and Systems Technologies (MOCAST)},

abstract = {The transformation of the conventional electricity grid into a new paradigm called smart grid demands the appropriate cybersecurity solutions. In this paper, we focus on the security of the IEC 60870-5-104 (IEC-104) protocol which is commonly used by Supervisory Control and Data Acquisition (SCADA) systems in the energy domain. In particular, after investigating its security issues, we provide a multivariate Intrusion Detection System (IDS) which adopts both access control and outlier detection mechanisms in order to detect timely possible anomalies against IEC-104. The efficiency of the proposed IDS is reflected by the Accuracy and F1 metrics that reach 98% and 87%, respectively.},

keywords = {Anomaly Detection, Cybersecurity, Data Acquisition, IEC-60870- 5-104},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{9200256,

title = {Trust Management in Smart Grid: A Markov Trust Model},

author = {Dimitrios Pliatsios and Panagiotis Sarigiannidis and Georgios Efstathopoulos and Antonios Sarigiannidis and Apostolos Tsiakalos},

url = {https://ieeexplore.ieee.org/document/9200256},

doi = {10.1109/MOCAST49295.2020.9200256},

year  = {2020},

date = {2020-01-01},

booktitle = {2020 9th International Conference on Modern Circuits and Systems Technologies (MOCAST)},

pages = {1-4},

abstract = {By leveraging the advancements in Information and Communication Technologies (ICT), Smart Grid (SG) aims to modernize the traditional electric power grid towards efficient distribution and reliable management of energy in the electrical domain. The SG Advanced Metering Infrastructure (AMI) contains numerous smart meters, which are deployed throughout the distribution grid. However, these smart meters are susceptible to cyberthreats that aim to disrupt the normal operation of the SG. Cyberattacks can have various consequences in the smart grid, such as incorrect customer billing or equipment destruction. Therefore, these devices should operate on a trusted basis in order to ensure the availability, confidentiality, and integrity of the metering data. In this paper, we propose a Markov chain trust model that determines the Trust Value (TV) for each AMI device based on its behavior. Finally, numerical computations were carried out in order to investigate the reaction of the proposed model to the behavior changes of a device.},

keywords = {},

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{8806693,

title = {A Survey On Honeypots, Honeynets And Their Applications On Smart Grid},

author = {Christos Dalamagkas and Panagiotis Sarigiannidis and Dimosthenis Ioannidis and Eider Iturbe and Odysseas Nikolis and Francisco Ramos and Erkuden Rios and Antonios Sarigiannidis and Dimitrios Tzovaras},

url = {https://ieeexplore.ieee.org/abstract/document/8806693},

doi = {10.1109/NETSOFT.2019.8806693},

year  = {2019},

date = {2019-01-01},

booktitle = {2019 IEEE Conference on Network Softwarization (NetSoft)},

pages = {93-100},

abstract = {Power grid is a major part of modern Critical Infrastructure (CIN). The rapid evolution of Information and Communication Technologies (ICT) enables traditional power grids to encompass advanced technologies that allow them to monitor their state, increase their reliability, save costs and provide ICT services to end customers, thus converting them into smart grids. However, smart grid is exposed to several security threats, as hackers might try to exploit vulnerabilities of the industrial infrastructure and cause disruption to national electricity system with severe consequences to citizens and commerce. This paper investigates and compares honey-x technologies that could be applied to smart grid in order to distract intruders, obtain attack strategies, protect the real infrastructure and form forensic evidence to be used in court.},

keywords = {},

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}

}

@inproceedings{triantafyllou2018towards,

title = {Towards an anonymous incident communication channel for electric smart grids},

author = {Anna Triantafyllou and Panagiotis Sarigiannidis and Antonios Sarigiannidis and Erkuden Rios and Eider Iturbe},

url = {https://zenodo.org/record/3831483},

doi = {/3291533.3291559},

year  = {2018},

date = {2018-11-29},

urldate = {2018-01-01},

booktitle = {Proceedings of the 22nd Pan-Hellenic Conference on Informatics},

pages = {34--39},

publisher = {PCI '18: Proceedings of the 22nd Pan-Hellenic Conference on Informatics},

abstract = {



The Electric Smart Grid (ESG) is an intelligent critical infrastructure aiming to create an automated and distributed advanced energy delivery network, while preserving information privacy. This study proposes the implementation of an Anonymous Incident Communication Channel (AICC) amongst smart grids across Europe to improve situational awareness and enhance security of the new electric intelligent infrastructures. All participating organizations will have the ability to broadcast sensitive information, stored anonymously in a repository, without exposing the reputation of the organisation. This work focuses on the requirements of establishment, the possible obstacles and proposed data protection techniques to be applied in the AICC. Furthermore, a discussion is conducted regarding the documentation of cyber-incidents. Last but not least, the benefits and the potential risks of this AICC concept are also provided.

},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}