2022
Athanasios Liatifis, Christos Dalamagkas, Panagiotis Radoglou-Grammatikis, Thomas Lagkas, Evangelos Markakis, Valeri Mladenov, Panagiotis Sarigiannidis
Fault-Tolerant SDN Solution for Cybersecurity Applications Conference Paper
Proceedings of the 17th International Conference on Availability, Reliability and Security, ARES '22 Association for Computing Machinery, Vienna, Austria, 2022, ISBN: 9781450396707.
Περίληψη | BibTeX | Ετικέτες: Cybersecurity, Smart Grid, Software Defined Networking | Σύνδεσμοι:
@conference{10.1145/3538969.3544479b,
title = {Fault-Tolerant SDN Solution for Cybersecurity Applications},
author = {Athanasios Liatifis and Christos Dalamagkas and Panagiotis Radoglou-Grammatikis and Thomas Lagkas and Evangelos Markakis and Valeri Mladenov and Panagiotis Sarigiannidis},
url = {https://doi.org/10.1145/3538969.3544479
},
doi = {10.1145/3538969.3544479},
isbn = {9781450396707},
year = {2022},
date = {2022-08-23},
booktitle = {Proceedings of the 17th International Conference on Availability, Reliability and Security},
publisher = {Association for Computing Machinery},
address = {Vienna, Austria},
series = {ARES '22},
abstract = {The rapid growth of computer networks in various sectors has led to new services previously hard or impossible to implement. Internet of Things has also assisted in this evolution offering easy access to data but at the same time imposing constraints on both security and quality of service. In this paper, an SDN fault tolerant and resilient SDN controller design approach is presented. The proposed solution is suitable for a wide range of environments. Benefits stemming from actual scenarios are presented and discussed among other solutions.},
keywords = {Cybersecurity, Smart Grid, Software Defined Networking},
pubstate = {published},
tppubtype = {conference}
}
The rapid growth of computer networks in various sectors has led to new services previously hard or impossible to implement. Internet of Things has also assisted in this evolution offering easy access to data but at the same time imposing constraints on both security and quality of service. In this paper, an SDN fault tolerant and resilient SDN controller design approach is presented. The proposed solution is suitable for a wide range of environments. Benefits stemming from actual scenarios are presented and discussed among other solutions.
2021
Panagiotis Radoglou Grammatikis, Panagiotis Sarigiannidis, Christos Dalamagkas, Yannis Spyridis, Thomas Lagkas, Georgios Efstathopoulos, Achilleas Sesis, Ignacio Labrador Pavon, Ruben Trapero Burgos, Rodrigo Diaz, Antonios Sarigiannidis, Dimitris Papamartzivanos, Sofia Anna Menesidou, Giannis Ledakis, Achilleas Pasias, Thanasis Kotsiopoulos, Anastasios Drosou, Orestis Mavropoulos, Alba Colet Subirachs, Pol Paradell Sola, José Luis Domínguez-García, Marisa Escalante, Molinuevo Martin Alberto, Benito Caracuel, Francisco Ramos, Vasileios Gkioulos, Sokratis Katsikas, Hans Christian Bolstad, Dan-Eric Archer, Nikola Paunovic, Ramon Gallart, Theodoros Rokkas, Alicia Arce
SDN-Based Resilient Smart Grid: The SDN-microSENSE Architecture Journal Article
In: Digital, vol. 1, no. 4, pp. 173–187, 2021, ISSN: 2673-6470.
Περίληψη | BibTeX | Ετικέτες: Anomaly Detection, Blockchain, Cybersecurity, energy management; honeypots, intrusiondetection, islanding, Privacy, Smart Grid, Software Defined Networking | Σύνδεσμοι:
@article{digital1040013,
title = {SDN-Based Resilient Smart Grid: The SDN-microSENSE Architecture},
author = { Panagiotis Radoglou Grammatikis and Panagiotis Sarigiannidis and Christos Dalamagkas and Yannis Spyridis and Thomas Lagkas and Georgios Efstathopoulos and Achilleas Sesis and Ignacio Labrador Pavon and Ruben Trapero Burgos and Rodrigo Diaz and Antonios Sarigiannidis and Dimitris Papamartzivanos and Sofia Anna Menesidou and Giannis Ledakis and Achilleas Pasias and Thanasis Kotsiopoulos and Anastasios Drosou and Orestis Mavropoulos and Alba Colet Subirachs and Pol Paradell Sola and José Luis Domínguez-García and Marisa Escalante and Molinuevo Martin Alberto and Benito Caracuel and Francisco Ramos and Vasileios Gkioulos and Sokratis Katsikas and Hans Christian Bolstad and Dan-Eric Archer and Nikola Paunovic and Ramon Gallart and Theodoros Rokkas and Alicia Arce},
url = {https://www.researchgate.net/publication/354992483_SDN-Based_Resilient_Smart_Grid_The_SDN-microSENSE_Architecture},
doi = {10.3390/digital1040013},
issn = {2673-6470},
year = {2021},
date = {2021-09-24},
journal = {Digital},
volume = {1},
number = {4},
pages = {173--187},
abstract = {The technological leap of smart technologies and the Internet of Things has advanced the conventional model of the electrical power and energy systems into a new digital era, widely known as the Smart Grid. The advent of Smart Grids provides multiple benefits, such as self-monitoring, self-healing and pervasive control. However, it also raises crucial cybersecurity and privacy concerns that can lead to devastating consequences, including cascading effects with other critical infrastructures or even fatal accidents. This paper introduces a novel architecture, which will increase the Smart Grid resiliency, taking full advantage of the Software-Defined Networking (SDN) technology. The proposed architecture called SDN-microSENSE architecture consists of three main tiers: (a) Risk assessment, (b) intrusion detection and correlation and (c) self-healing. The first tier is responsible for evaluating dynamically the risk level of each Smart Grid asset. The second tier undertakes to detect and correlate security events and, finally, the last tier mitigates the potential threats, ensuring in parallel the normal operation of the Smart Grid. It is noteworthy that all tiers of the SDN-microSENSE architecture interact with the SDN controller either for detecting or mitigating intrusions.},
keywords = {Anomaly Detection, Blockchain, Cybersecurity, energy management; honeypots, intrusiondetection, islanding, Privacy, Smart Grid, Software Defined Networking},
pubstate = {published},
tppubtype = {article}
}
The technological leap of smart technologies and the Internet of Things has advanced the conventional model of the electrical power and energy systems into a new digital era, widely known as the Smart Grid. The advent of Smart Grids provides multiple benefits, such as self-monitoring, self-healing and pervasive control. However, it also raises crucial cybersecurity and privacy concerns that can lead to devastating consequences, including cascading effects with other critical infrastructures or even fatal accidents. This paper introduces a novel architecture, which will increase the Smart Grid resiliency, taking full advantage of the Software-Defined Networking (SDN) technology. The proposed architecture called SDN-microSENSE architecture consists of three main tiers: (a) Risk assessment, (b) intrusion detection and correlation and (c) self-healing. The first tier is responsible for evaluating dynamically the risk level of each Smart Grid asset. The second tier undertakes to detect and correlate security events and, finally, the last tier mitigates the potential threats, ensuring in parallel the normal operation of the Smart Grid. It is noteworthy that all tiers of the SDN-microSENSE architecture interact with the SDN controller either for detecting or mitigating intrusions.
D. Pliatsios, P. Sarigiannidis, G. Fragulis, A. Tsiakalos, D. Margounakis
A Dynamic Recommendation-based Trust Scheme for the Smart Grid Conference Paper
3rd International Workshop on Cyber-Security Threats, Trust and Privacy management in Software-defined and Virtualized Infrastructures (SecSoft 2021), 2021.
Περίληψη | BibTeX | Ετικέτες: cyberattacks, security, Smart Grid, smart meters, trust management | Σύνδεσμοι:
@conference{Pliatsios2021,
title = {A Dynamic Recommendation-based Trust Scheme for the Smart Grid},
author = {D. Pliatsios and P. Sarigiannidis and G. Fragulis and A. Tsiakalos and D. Margounakis},
url = {https://www.researchgate.net/publication/353484865_A_Dynamic_Recommendation-based_Trust_Scheme_for_the_Smart_Grid},
doi = {10.1109/NetSoft51509.2021.9492542 +Date: 26 July 2021},
year = {2021},
date = {2021-07-26},
booktitle = {3rd International Workshop on Cyber-Security Threats, Trust and Privacy management in Software-defined and Virtualized Infrastructures (SecSoft 2021)},
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 nonadversarial 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 = {cyberattacks, security, Smart Grid, smart meters, trust management},
pubstate = {published},
tppubtype = {conference}
}
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 nonadversarial 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.
Ilias Siniosoglou, Panagiotis Radoglou-Grammatikis, Georgios Efstathopoulos, Panagiotis Fouliras, Panagiotis Sarigiannidis
A Unified Deep Learning Anomaly Detection and Classification Approach for Smart Grid Environments Journal Article
In: {IEEE} Transactions on Network and Service Management, vol. 1, no. 1, pp. 1, 2021.
Περίληψη | BibTeX | Ετικέτες: Anomaly Detection, Auto-encoder, Cybersecurity, Deep Learning, Generative Adversarial Network, machine learning, Modbus, Smart Grid | Σύνδεσμοι:
@article{Siniosoglou2021b,
title = {A Unified Deep Learning Anomaly Detection and Classification Approach for Smart Grid Environments},
author = {Ilias Siniosoglou and Panagiotis Radoglou-Grammatikis and Georgios Efstathopoulos and Panagiotis Fouliras and Panagiotis Sarigiannidis},
url = {https://www.researchgate.net/publication/351344684_A_Unified_Deep_Learning_Anomaly_Detection_and_Classification_Approach_for_Smart_Grid_Environments},
doi = {10.1109/TNSM.2021.3078381},
year = {2021},
date = {2021-05-07},
journal = {{IEEE} Transactions on Network and Service Management},
volume = {1},
number = {1},
pages = {1},
abstract = {The interconnected and heterogeneous nature of the next-generation Electrical Grid (EG), widely known as Smart Grid (SG), bring severe cybersecurity and privacy risks that can also raise domino effects against other Critical Infrastructures (CIs). In this paper, we present an Intrusion Detection System (IDS) specially designed for the SG environments that use Modbus/Transmission Control Protocol (TCP) and Distributed Network Protocol 3 (DNP3) protocols. The proposed IDS called MENSA (anoMaly dEtection aNd claSsificAtion) adopts a novel Autoencoder-Generative Adversarial Network (GAN) architecture for (a) detecting operational anomalies and (b) classifying Modbus/TCP and DNP3 cyberattacks. In particular, MENSA combines the aforementioned Deep Neural Networks (DNNs) in a common architecture, taking into account the adversarial loss and the reconstruction difference. The proposed IDS is validated in four real SG evaluation environments, namely (a) SG lab, (b) substation, (c) hydropower plant and (d) power plant, solving successfully an outlier detection (i.e., anomaly detection) problem as well as a challenging multiclass classification problem consisting of 14 classes (13 Modbus/TCP cyberattacks and normal instances). Furthermore, MENSA can discriminate five cyberattacks against DNP3. The evaluation results demonstrate the efficiency of MENSA compared to other Machine Learning (ML) and Deep Learning (DL) methods in terms of Accuracy, False Positive Rate (FPR), True Positive Rate (TPR) and the F1 score.},
keywords = {Anomaly Detection, Auto-encoder, Cybersecurity, Deep Learning, Generative Adversarial Network, machine learning, Modbus, Smart Grid},
pubstate = {published},
tppubtype = {article}
}
The interconnected and heterogeneous nature of the next-generation Electrical Grid (EG), widely known as Smart Grid (SG), bring severe cybersecurity and privacy risks that can also raise domino effects against other Critical Infrastructures (CIs). In this paper, we present an Intrusion Detection System (IDS) specially designed for the SG environments that use Modbus/Transmission Control Protocol (TCP) and Distributed Network Protocol 3 (DNP3) protocols. The proposed IDS called MENSA (anoMaly dEtection aNd claSsificAtion) adopts a novel Autoencoder-Generative Adversarial Network (GAN) architecture for (a) detecting operational anomalies and (b) classifying Modbus/TCP and DNP3 cyberattacks. In particular, MENSA combines the aforementioned Deep Neural Networks (DNNs) in a common architecture, taking into account the adversarial loss and the reconstruction difference. The proposed IDS is validated in four real SG evaluation environments, namely (a) SG lab, (b) substation, (c) hydropower plant and (d) power plant, solving successfully an outlier detection (i.e., anomaly detection) problem as well as a challenging multiclass classification problem consisting of 14 classes (13 Modbus/TCP cyberattacks and normal instances). Furthermore, MENSA can discriminate five cyberattacks against DNP3. The evaluation results demonstrate the efficiency of MENSA compared to other Machine Learning (ML) and Deep Learning (DL) methods in terms of Accuracy, False Positive Rate (FPR), True Positive Rate (TPR) and the F1 score.
T. Kotsiopoulos, P. Sarigiannidis, D. Ioannidis, D. Tzovaras
Machine Learning and Deep Learning in smart manufacturing: The Smart Grid paradigm Journal Article
In: Computer Science Review, vol. 40, pp. 100341, 2021.
Περίληψη | BibTeX | Ετικέτες: Deep Learning, Industrial AI, Industry 4.0, machine learning, Smart Grid | Σύνδεσμοι:
@article{Kotsiopoulos2021,
title = {Machine Learning and Deep Learning in smart manufacturing: The Smart Grid paradigm},
author = { T. Kotsiopoulos and P. Sarigiannidis and D. Ioannidis and D. Tzovaras},
url = {https://www.researchgate.net/publication/346545781_Machine_Learning_and_Deep_Learning_in_Smart_Manufacturing_The_Smart_Grid_Paradigm},
doi = {10.1016/j.cosrev.2020.100341},
year = {2021},
date = {2021-05-01},
journal = {Computer Science Review},
volume = {40},
pages = {100341},
publisher = {Elsevier BV},
abstract = {Industry 4.0 is the new industrial revolution. By connecting every machine and activity through network sensors to the Internet, a huge amount of data is generated. Machine Learning (ML) and Deep Learning (DL) are two subsets of Artificial Intelligence (AI), which are used to evaluate the generated data and produce valuable information about the manufacturing enterprise, while introducing in parallel the Industrial AI (IAI). In this paper, the principles of the Industry 4.0 are highlighted, by giving emphasis to the features, requirements, and challenges behind Industry 4.0. In addition, a new architecture for AIA is presented. Furthermore, the most important ML and DL algorithms used in Industry 4.0 are presented and compiled in detail. Each algorithm is discussed and evaluated in terms of its features, its applications, and its efficiency. Then, we focus on one of the most important Industry 4.0 fields, namely the smart grid, where ML and DL models are presented and analyzed in terms of efficiency and effectiveness in smart grid applications. Lastly, trends and challenges in the field of data analysis in the context of the new Industrial era are highlighted and discussed such as scalability, cybersecurity, and big data.},
keywords = {Deep Learning, Industrial AI, Industry 4.0, machine learning, Smart Grid},
pubstate = {published},
tppubtype = {article}
}
Industry 4.0 is the new industrial revolution. By connecting every machine and activity through network sensors to the Internet, a huge amount of data is generated. Machine Learning (ML) and Deep Learning (DL) are two subsets of Artificial Intelligence (AI), which are used to evaluate the generated data and produce valuable information about the manufacturing enterprise, while introducing in parallel the Industrial AI (IAI). In this paper, the principles of the Industry 4.0 are highlighted, by giving emphasis to the features, requirements, and challenges behind Industry 4.0. In addition, a new architecture for AIA is presented. Furthermore, the most important ML and DL algorithms used in Industry 4.0 are presented and compiled in detail. Each algorithm is discussed and evaluated in terms of its features, its applications, and its efficiency. Then, we focus on one of the most important Industry 4.0 fields, namely the smart grid, where ML and DL models are presented and analyzed in terms of efficiency and effectiveness in smart grid applications. Lastly, trends and challenges in the field of data analysis in the context of the new Industrial era are highlighted and discussed such as scalability, cybersecurity, and big data.
P. Radoglou-Grammatikis, P. Sarigiannidis, E. Iturbe, E. Rios, S. Martinez, A. Sarigiannidis, G. Eftathopoulos, I. Spyridis, A. Sesis, N. Vakakis, D. Tzovaras, E. Kafetzakis, I. Giannoulakis, M. Tzifas, A. Giannakoulias, M. Angelopoulos, F. Ramos
SPEAR SIEM: A Security Information and Event Management system for the Smart Grid Journal Article
In: Computer Networks, pp. 108008, 2021.
Περίληψη | BibTeX | Ετικέτες: Anomaly Detection, Cybersecurity, Deep Learning, Intrusion detection, machine learning, SCADA, Security Information and Event Management, Smart Grid | Σύνδεσμοι:
@article{RadoglouGrammatikis2021,
title = {SPEAR SIEM: A Security Information and Event Management system for the Smart Grid},
author = { P. Radoglou-Grammatikis and P. Sarigiannidis and E. Iturbe and E. Rios and S. Martinez and A. Sarigiannidis and G. Eftathopoulos and I. Spyridis and A. Sesis and N. Vakakis and D. Tzovaras and E. Kafetzakis and I. Giannoulakis and M. Tzifas and A. Giannakoulias and M. Angelopoulos and F. Ramos},
url = {https://www.researchgate.net/publication/350287201_SPEAR_SIEM_A_Security_Information_and_Event_Management_system_for_the_Smart_Grid},
doi = {10.1016/j.comnet.2021.108008},
year = {2021},
date = {2021-04-01},
journal = {Computer Networks},
pages = {108008},
publisher = {Elsevier BV},
abstract = {The technological leap of smart technologies has brought the conventional electrical grid in a new digital era called Smart Grid (SG), providing multiple benefits, such as two-way communication, pervasive control and self-healing. However, this new reality generates significant cybersecurity risks due to the heterogeneous and insecure nature of SG. In particular, SG relies on legacy communication protocols that have not been implemented having cybersecurity in mind. Moreover, the advent of the Internet of Things (IoT) creates severe cybersecurity challenges. The Security Information and Event Management (SIEM) systems constitute an emerging technology in the cybersecurity area, having the capability to detect, normalise and correlate a vast amount of security events. They can orchestrate the entire security of a smart ecosystem, such as SG. Nevertheless, the current SIEM systems do not take into account the unique SG peculiarities and characteristics like the legacy communication protocols. In this paper, we present the Secure and PrivatE smArt gRid (SPEAR) SIEM, which focuses on SG. The main contribution of our work is the design and implementation of a SIEM system capable of detecting, normalising and correlating cyberattacks and anomalies against a plethora of SG application-layer protocols. It is noteworthy that the detection performance of the SPEAR SIEM is demonstrated with real data originating from four real SG use case (a) hydropower plant, (b) substation, (c) power plant and (d) smart home.},
keywords = {Anomaly Detection, Cybersecurity, Deep Learning, Intrusion detection, machine learning, SCADA, Security Information and Event Management, Smart Grid},
pubstate = {published},
tppubtype = {article}
}
The technological leap of smart technologies has brought the conventional electrical grid in a new digital era called Smart Grid (SG), providing multiple benefits, such as two-way communication, pervasive control and self-healing. However, this new reality generates significant cybersecurity risks due to the heterogeneous and insecure nature of SG. In particular, SG relies on legacy communication protocols that have not been implemented having cybersecurity in mind. Moreover, the advent of the Internet of Things (IoT) creates severe cybersecurity challenges. The Security Information and Event Management (SIEM) systems constitute an emerging technology in the cybersecurity area, having the capability to detect, normalise and correlate a vast amount of security events. They can orchestrate the entire security of a smart ecosystem, such as SG. Nevertheless, the current SIEM systems do not take into account the unique SG peculiarities and characteristics like the legacy communication protocols. In this paper, we present the Secure and PrivatE smArt gRid (SPEAR) SIEM, which focuses on SG. The main contribution of our work is the design and implementation of a SIEM system capable of detecting, normalising and correlating cyberattacks and anomalies against a plethora of SG application-layer protocols. It is noteworthy that the detection performance of the SPEAR SIEM is demonstrated with real data originating from four real SG use case (a) hydropower plant, (b) substation, (c) power plant and (d) smart home.
2020
D. Pliatsios, P. Sarigiannidis, K. Psannis, S. K. Goudos, V. Vitsas, I. Moscholios
Big Data against Security Threats: The SPEAR Intrusion Detection System Conference Paper
2020 3rd World Symposium on Communication Engineering (WSCE), IEEE, 2020.
Περίληψη | BibTeX | Ετικέτες: Μεγάλα Δεδομένα και Ευφυείς Εφαρμογές στο Διαδίκτυο των Πραγμάτων, Cyber Attack, intrusion detection system, Smart Grid | Σύνδεσμοι:
@conference{Pliatsios202012,
title = {Big Data against Security Threats: The SPEAR Intrusion Detection System},
author = { D. Pliatsios and P. Sarigiannidis and K. Psannis and S. K. Goudos and V. Vitsas and I. Moscholios},
doi = {10.1109/wsce51339.2020.9275580},
year = {2020},
date = {2020-10-01},
booktitle = {2020 3rd World Symposium on Communication Engineering (WSCE)},
journal = {2020 3rd World Symposium on Communication Engineering, WSCE 2020},
pages = {12-17},
publisher = {IEEE},
abstract = {The environmental concerns, the limited availability of conventional energy sources, the integration of alternative energy sources and the increasing number of power-demanding appliances change the way electricity is generated and distributed. Smart Grid (SG) is an appealing concept, which was developed in response to the emerging issues of electricity generation and distribution. By leveraging the latest advancements of Information and Communication Technologies (ICT), it offers significant benefits to energy providers, retailers and consumers. Nevertheless, SG is vulnerable to cyber attacks, that could cause critical economic and ecological consequences. Traditional Intrusion Detection Systems (IDSs) are becoming less efficient in detecting and mitigating cyberattacks, due to their limited capabilities of analyzing the exponentially increasing volume of network traffic. In this paper, we present the Secure and PrivatE smArt gRid (SPEAR) platform, which features a Big Data enabled IDS that timely detects and identifies cyber attacks against SG components. In order to validate the efficiency of the SPEAR platform regarding the protection of critical infrastructure, we installed the platform in a small wind power plant. © 2020 IEEE.},
keywords = {Big Data, Cyber Attack, intrusion detection system, Smart Grid},
pubstate = {published},
tppubtype = {conference}
}
The environmental concerns, the limited availability of conventional energy sources, the integration of alternative energy sources and the increasing number of power-demanding appliances change the way electricity is generated and distributed. Smart Grid (SG) is an appealing concept, which was developed in response to the emerging issues of electricity generation and distribution. By leveraging the latest advancements of Information and Communication Technologies (ICT), it offers significant benefits to energy providers, retailers and consumers. Nevertheless, SG is vulnerable to cyber attacks, that could cause critical economic and ecological consequences. Traditional Intrusion Detection Systems (IDSs) are becoming less efficient in detecting and mitigating cyberattacks, due to their limited capabilities of analyzing the exponentially increasing volume of network traffic. In this paper, we present the Secure and PrivatE smArt gRid (SPEAR) platform, which features a Big Data enabled IDS that timely detects and identifies cyber attacks against SG components. In order to validate the efficiency of the SPEAR platform regarding the protection of critical infrastructure, we installed the platform in a small wind power plant. © 2020 IEEE.
V. Mladenov, V. Chobanov, P. Sarigiannidis, P. I. Radoglou-Grammatikis, A. Hristov, P. Zlatev
Defense against cyber-attacks on the Hydro Power Plant connected in parallel with Energy System Conference Paper
2020 12th Electrical Engineering Faculty Conference (BulEF), IEEE, 2020.
Περίληψη | BibTeX | Ετικέτες: Cyber security, Energy System, Grid infrastructure, Hydro Power Plant, Smart Grid | Σύνδεσμοι:
@conference{Mladenov2020,
title = {Defense against cyber-attacks on the Hydro Power Plant connected in parallel with Energy System},
author = { V. Mladenov and V. Chobanov and P. Sarigiannidis and P. I. Radoglou-Grammatikis and A. Hristov and P. Zlatev},
url = {https://www.researchgate.net/publication/348642791_Defense_against_cyber-attacks_on_the_Hydro_Power_Plant_connected_in_parallel_with_Energy_System},
doi = {10.1109/bulef51036.2020.9326016},
year = {2020},
date = {2020-09-01},
booktitle = {2020 12th Electrical Engineering Faculty Conference (BulEF)},
journal = {2020 12th Electrical Engineering Faculty Conference, BulEF 2020},
publisher = {IEEE},
abstract = {In today's modern energy sector, driven more and more towards decentralization, which includes many smaller energy producers rather than huge government projects, security against cyber-attacks is becoming more crucial for the energy grid. Since many small energy plants do not have the resources to finance very expensive existing cyber-security systems, they often have no security system in place at all. Although with small energy producers, the risks of being under attack are not as devastating as in a huge power plants, they still pose a serious threat to the energy system and to the supply of electricity to whole regions. Moreover, in the era of technology, such cyber-attacks could be carried out simultaneously at many locations, thus risking the lack of electricity to larger areas. Since there was a clearly identified need for such an instrument, the SPEAR consortium, started to develop tailor made solution for different types of actors in the energy sector, to prevent such occurrences and help secure the energy system. One of the use cases, investigated in the project, is a real operating hydro power plant in the mountain area of Bulgaria called Leshnitsa, which will be one of the four sites to first test the functionality of the finished product. The plant had no previous cyber-security system in place and had already experienced one attack, where one of the computers in the plant was hacked and a ransom was demanded from the attackers to unlock it. Exactly events like this one are proof, that the energy sector has a need to protect the growing number of small independent actors in the energy system.. © 2020 IEEE.},
keywords = {Cyber security, Energy System, Grid infrastructure, Hydro Power Plant, Smart Grid},
pubstate = {published},
tppubtype = {conference}
}
In today's modern energy sector, driven more and more towards decentralization, which includes many smaller energy producers rather than huge government projects, security against cyber-attacks is becoming more crucial for the energy grid. Since many small energy plants do not have the resources to finance very expensive existing cyber-security systems, they often have no security system in place at all. Although with small energy producers, the risks of being under attack are not as devastating as in a huge power plants, they still pose a serious threat to the energy system and to the supply of electricity to whole regions. Moreover, in the era of technology, such cyber-attacks could be carried out simultaneously at many locations, thus risking the lack of electricity to larger areas. Since there was a clearly identified need for such an instrument, the SPEAR consortium, started to develop tailor made solution for different types of actors in the energy sector, to prevent such occurrences and help secure the energy system. One of the use cases, investigated in the project, is a real operating hydro power plant in the mountain area of Bulgaria called Leshnitsa, which will be one of the four sites to first test the functionality of the finished product. The plant had no previous cyber-security system in place and had already experienced one attack, where one of the computers in the plant was hacked and a ransom was demanded from the attackers to unlock it. Exactly events like this one are proof, that the energy sector has a need to protect the growing number of small independent actors in the energy system.. © 2020 IEEE.
P. Radoglou-Grammatikis, P. Sarigiannidis, E. Iturbe, E. Rios, A. Sarigiannidis, O. Nikolis, D. Ioannidis, V. Machamint, M. Tzifas, A. Giannakoulias, M. Angelopoulos, A. Papadopoulos, F. Ramos
Secure and private smart grid: The SPEAR architecture Conference Paper
2020 6th IEEE Conference on Network Softwarization (NetSoft), IEEE, 2020.
Περίληψη | BibTeX | Ετικέτες: Anomaly Detection, Anonymity, Cybersecurity, Forensics, Honeypots, Intrusion detection, Privacy, Smart Grid | Σύνδεσμοι:
@conference{Grammatikis2020450,
title = {Secure and private smart grid: The SPEAR architecture},
author = { P. Radoglou-Grammatikis and P. Sarigiannidis and E. Iturbe and E. Rios and A. Sarigiannidis and O. Nikolis and D. Ioannidis and V. Machamint and M. Tzifas and A. Giannakoulias and M. Angelopoulos and A. Papadopoulos and F. Ramos},
url = {https://www.researchgate.net/publication/343621502_Secure_and_Private_Smart_Grid_The_SPEAR_Architecture?_sg=ajSET8e8bb-KvKba1e9QHd7a7IFuKtI-72RhxDMcm-yozF1Q-5Jx4b8jAVrAhVncE1vtLBx2eVdgcx4},
doi = {10.1109/NetSoft48620.2020.9165420},
year = {2020},
date = {2020-06-01},
booktitle = {2020 6th IEEE Conference on Network Softwarization (NetSoft)},
journal = {Proceedings of the 2020 IEEE Conference on Network Softwarization: Bridging the Gap Between AI and Network Softwarization, NetSoft 2020},
pages = {450-456},
publisher = {IEEE},
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. © 2020 IEEE.},
keywords = {Anomaly Detection, Anonymity, Cybersecurity, Forensics, Honeypots, Intrusion detection, Privacy, Smart Grid},
pubstate = {published},
tppubtype = {conference}
}
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. © 2020 IEEE.
D. Pliatsios, P. Sarigiannidis, G. Efstathopoulos, A. Sarigiannidis, A. Tsiakalos
Trust Management in Smart Grid: A Markov Trust Model Conference Paper
2020.
Περίληψη | BibTeX | Ετικέτες: Advanced Metering Infrastructure, Cybersecurity, Markov Model, Smart Grid, Trust Model | Σύνδεσμοι:
@conference{Pliatsios2020b,
title = {Trust Management in Smart Grid: A Markov Trust Model},
author = { D. Pliatsios and P. Sarigiannidis and G. Efstathopoulos and A. Sarigiannidis and A. Tsiakalos},
url = {https://www.researchgate.net/publication/345186037_Trust_Management_in_Smart_Grid_A_Markov_Trust_Model},
doi = {10.1109/MOCAST49295.2020.9200256},
year = {2020},
date = {2020-01-01},
journal = {2020 9th International Conference on Modern Circuits and Systems Technologies, MOCAST 2020},
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. © 2020 IEEE.},
keywords = {Advanced Metering Infrastructure, Cybersecurity, Markov Model, Smart Grid, Trust Model},
pubstate = {published},
tppubtype = {conference}
}
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. © 2020 IEEE.
Διεύθυνση
Internet of Things and Applications Lab
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University of Western Macedonia Campus
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Greece
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