2021
I. Siniosoglou; P. Sarigiannidis; V. Argyriou; T. Lagkas; S. Goudos; M. Poveda
Federated Intrusion Detection In NG-IoT Healthcare Systems: An Adversarial Approach Conference
2021 IEEE International Conference on Communications (ICC), 2021.
Abstract | BibTeX | Tags: | Links:
@conference{Siniosoglou2021,
title = {Federated Intrusion Detection In NG-IoT Healthcare Systems: An Adversarial Approach},
author = { I. Siniosoglou and P. Sarigiannidis and V. Argyriou and T. Lagkas and S. Goudos and M. Poveda},
url = {https://www.researchgate.net/publication/349158602_Federated_Intrusion_Detection_In_NG-IoT_Healthcare_Systems_An_Adversarial_Approach},
doi = {10.1109/ICC42927.2021.9500578},
year = {2021},
date = {2021-06-14},
booktitle = {2021 IEEE International Conference on Communications (ICC)},
journal = {IEEE International Conference on Communications},
abstract = {In recent years and with the advancement of IoT networks, malicious intrusions aiming at disrupting the services and getting access to confidential information in medical environments is ever progressing. To that end, this paper proposes a Federated Layered Architecture to be used in Medical CyberPhysical Systems (MCPS) Networks that entails the creation of multiple aggregation layers to induce further security to the model training process. Moreover, two Deep Adversarial Neural Networks (GANs) are presented for use with data found in the MCPS environment. The evaluation of the presented work showed that the models trained in the Federated system have an increase in their ability to detect possible intrusions in the MCPS network than the commonly trained models. © 2021 IEEE.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
In recent years and with the advancement of IoT networks, malicious intrusions aiming at disrupting the services and getting access to confidential information in medical environments is ever progressing. To that end, this paper proposes a Federated Layered Architecture to be used in Medical CyberPhysical Systems (MCPS) Networks that entails the creation of multiple aggregation layers to induce further security to the model training process. Moreover, two Deep Adversarial Neural Networks (GANs) are presented for use with data found in the MCPS environment. The evaluation of the presented work showed that the models trained in the Federated system have an increase in their ability to detect possible intrusions in the MCPS network than the commonly trained models. © 2021 IEEE.
P. Radoglou; P. Sarigiannidis; G. Efstathopoulos; T. Lagkas; G. Fragulis; A. Sarigiannidis
A Self-Learning Approach for Detecting Intrusions in Healthcare Systems Conference
2021 IEEE International Conference on Communications (ICC), 2021.
Abstract | BibTeX | Tags: Active Learning, Cybersecurity, Healthcare, Intrusion De-tection | Links:
@conference{Radoglou_icc2021,
title = {A Self-Learning Approach for Detecting Intrusions in Healthcare Systems},
author = { P. Radoglou and P. Sarigiannidis and G. Efstathopoulos and T. Lagkas and G. Fragulis and A. Sarigiannidis},
url = {https://www.researchgate.net/publication/349158703_A_Self-Learning_Approach_for_Detecting_Intrusions_in_Healthcare_Systems},
doi = {10.1109/ICC42927.2021.9500354},
year = {2021},
date = {2021-06-14},
booktitle = {2021 IEEE International Conference on Communications (ICC)},
journal = {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. © 2021 IEEE.},
keywords = {Active Learning, Cybersecurity, Healthcare, Intrusion De-tection},
pubstate = {published},
tppubtype = {conference}
}
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. © 2021 IEEE.
V. Kelli; P. Sarigiannidis; T. Lagkas; V. Vitsas
A Cyber Resilience Framework for NG-IoT Healthcare Using Machine Learning and Blockchain Conference
2021 IEEE International Conference on Communications (ICC), 2021.
Abstract | BibTeX | Tags: | Links:
@conference{Kelli2021,
title = {A Cyber Resilience Framework for NG-IoT Healthcare Using Machine Learning and Blockchain},
author = { V. Kelli and P. Sarigiannidis and T. Lagkas and V. Vitsas},
url = {https://www.researchgate.net/publication/349158783_A_Cyber_Resilience_Framework_for_NG-IoT_Healthcare_Using_Machine_Learning_and_Blockchain},
doi = {10.1109/ICC42927.2021.9500496},
year = {2021},
date = {2021-06-14},
booktitle = {2021 IEEE International Conference on Communications (ICC)},
journal = {IEEE International Conference on Communications},
abstract = {Internet of Things (IoT) technology such as intelligent devices, sensors, actuators and wearables have been integrated in the healthcare industry, thus contributing in the creation of smart hospitals and remote assistance environments. Ensuring the eHealth network adopts the appropriate security measures in order to effectively protect sensitive patient data against malicious attempts is a tough challenge. Devices composing eHealth infrastructure are considered to be easily exploitable. To that end, a solution monitoring the intelligent healthcare environment is of essence. In addition, by digitalising all health records, appropriate measures need to be implemented in order for patient records to be accessible by authorized personnel only. Furthermore, creating interoperable systems, capable of being integrated by multiple organizations such as hospitals and insurance companies, while maintaining a General Data Protection Regulation-friendly posture, providing access to health data is a great importance for optimal patient assistance. To address both concerns, we present a framework featuring a multi-layer tool for providing a highly effective security solution specifically designed to address the eHealth requirements, and a blockchain access control component, based on smart contracts to provide access control for authorized users to patient records and health data in a distributed way.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Internet of Things (IoT) technology such as intelligent devices, sensors, actuators and wearables have been integrated in the healthcare industry, thus contributing in the creation of smart hospitals and remote assistance environments. Ensuring the eHealth network adopts the appropriate security measures in order to effectively protect sensitive patient data against malicious attempts is a tough challenge. Devices composing eHealth infrastructure are considered to be easily exploitable. To that end, a solution monitoring the intelligent healthcare environment is of essence. In addition, by digitalising all health records, appropriate measures need to be implemented in order for patient records to be accessible by authorized personnel only. Furthermore, creating interoperable systems, capable of being integrated by multiple organizations such as hospitals and insurance companies, while maintaining a General Data Protection Regulation-friendly posture, providing access to health data is a great importance for optimal patient assistance. To address both concerns, we present a framework featuring a multi-layer tool for providing a highly effective security solution specifically designed to address the eHealth requirements, and a blockchain access control component, based on smart contracts to provide access control for authorized users to patient records and health data in a distributed way.
S. Sotiroudis; K. Siakavara; G. Koudouridis; P. Sarigiannidis; S. Goudos
Enhancing Machine Learning Models for Path Loss Prediction Using Image Texture Techniques Journal Article
In: IEEE Antennas and Wireless Propagation Letters, vol. (Early Access), 2021.
Abstract | BibTeX | Tags: image texture, machine learning, mobile communications, pathloss prediction | Links:
@article{Sotiroudis2021b,
title = {Enhancing Machine Learning Models for Path Loss Prediction Using Image Texture Techniques},
author = {S. Sotiroudis and K. Siakavara and G. Koudouridis and P. Sarigiannidis and S. Goudos},
url = {https://www.researchgate.net/publication/352111245_Enhancing_Machine_Learning_Models_for_Path_Loss_Prediction_Using_Image_Texture_Techniques},
doi = {10.1109/LAWP.2021.3086180},
year = {2021},
date = {2021-06-03},
journal = {IEEE Antennas and Wireless Propagation Letters},
volume = {(Early Access)},
abstract = {The performance of machine learning-based path loss models relies heavily on the data they use at their inputs. Feature engineering is therefore essential for the models success. In the work at hand, we extract a new set of input features, based on image texture. The image that we use represents the footprint of the urban built-up area, where the gray scale values of the building blocks are analogue to their heights. We extract texture information by applying the Segmentation-based Fractal Texture Analysis algorithm on the orthogonal area that is bounded between the transmitter and the receiver. To the best of our knowledge this is the first time that such a technique is applied to a path loss modeling problem in electromagnetics. The algorithm thus delivers a new set of features, based on the images texture, which eventually reveal the built-up profile of the area. These new features are injected to an already existing feature set. Comparative analysis shows that the addition of texture-based features leads to enhanced predictions, for a diverse set of transmitter heights, machine learning algorithms, and performance metrics.},
keywords = {image texture, machine learning, mobile communications, pathloss prediction},
pubstate = {published},
tppubtype = {article}
}
The performance of machine learning-based path loss models relies heavily on the data they use at their inputs. Feature engineering is therefore essential for the models success. In the work at hand, we extract a new set of input features, based on image texture. The image that we use represents the footprint of the urban built-up area, where the gray scale values of the building blocks are analogue to their heights. We extract texture information by applying the Segmentation-based Fractal Texture Analysis algorithm on the orthogonal area that is bounded between the transmitter and the receiver. To the best of our knowledge this is the first time that such a technique is applied to a path loss modeling problem in electromagnetics. The algorithm thus delivers a new set of features, based on the images texture, which eventually reveal the built-up profile of the area. These new features are injected to an already existing feature set. Comparative analysis shows that the addition of texture-based features leads to enhanced predictions, for a diverse set of transmitter heights, machine learning algorithms, and performance metrics.
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.
Abstract | BibTeX | Tags: Anomaly Detection, Auto-encoder, Cybersecurity, Deep Learning, Generative Adversarial Network, machine learning, Modbus, Smart Grid | Links:
@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.
Abstract | BibTeX | Tags: Deep Learning, Industrial AI, Industry 4.0, machine learning, Smart Grid | Links:
@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.
V. Kelli; E.G. Sfakianakis; B. Ghita, P. Sarigiannidis
IoT Reference Architectures Book Chapter
In: Shiaeles, Stavros; Kolokotronis, Nicholas (Ed.): Internet of Things, Threats, Landscape, and Countermeasures , Chapter 2, CRC Press, 2021, ISBN: 9780367433321.
BibTeX | Tags: Internet of things, Wireless communication | Links:
@inbook{iot_reference_architectures,
title = {IoT Reference Architectures},
author = {V. Kelli and E.G. Sfakianakis and B. Ghita, P. Sarigiannidis},
editor = {Stavros Shiaeles and Nicholas Kolokotronis},
url = {https://www.routledge.com/Internet-of-Things-Threats-Landscape-and-Countermeasures/Shiaeles-Kolokotronis/p/book/9780367433321},
isbn = {9780367433321},
year = {2021},
date = {2021-04-29},
booktitle = {Internet of Things, Threats, Landscape, and Countermeasures },
publisher = {CRC Press},
chapter = {2},
keywords = {Internet of things, Wireless communication},
pubstate = {published},
tppubtype = {inbook}
}
P. Radoglou-Grammatikis; P. Sarigiannidis
Network Threats Book Chapter
In: Kolokotronis, Nicholas; Shiaeles, Stavros (Ed.): Cyber-Security Threats, Actors, and Dynamic Mitigation, Chapter 5, CRC Press, 2021, ISBN: 9780367433314.
BibTeX | Tags: Cybersecurity, network threats
@inbook{cybersecbook2021,
title = {Network Threats },
author = {P. Radoglou-Grammatikis and P. Sarigiannidis},
editor = {Nicholas Kolokotronis and Stavros Shiaeles},
isbn = {9780367433314},
year = {2021},
date = {2021-04-20},
booktitle = {Cyber-Security Threats, Actors, and Dynamic Mitigation},
publisher = {CRC Press},
chapter = {5},
keywords = {Cybersecurity, network threats},
pubstate = {published},
tppubtype = {inbook}
}
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.
Abstract | BibTeX | Tags: Anomaly Detection, Cybersecurity, Deep Learning, Intrusion detection, machine learning, SCADA, Security Information and Event Management, Smart Grid | Links:
@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.
A. Triantafyllou, P. Sarigiannidis, T. Lagkas, I. D. Moscholios; A. Sarigiannidis
Leveraging fairness in LoRaWAN: A novel scheduling scheme for collision avoidance Journal Article
In: Computer Networks, vol. 186, pp. 107735, 2021.
Abstract | BibTeX | Tags: Collision avoidance, Fairness, Internet of things, LoRa, LoRaWAN, Low-Power Wide Area Networks, Medium Access Control, Scalability | Links:
@article{Triantafyllou2021,
title = {Leveraging fairness in LoRaWAN: A novel scheduling scheme for collision avoidance},
author = { A. Triantafyllou, P. Sarigiannidis, T. Lagkas, I. D. Moscholios and A. Sarigiannidis},
url = {https://www.researchgate.net/publication/346627962_Leveraging_Fairness_in_LoRaWAN_A_Novel_Scheduling_Scheme_for_Collision_Avoidance},
doi = {10.1016/j.comnet.2020.107735},
year = {2021},
date = {2021-02-01},
journal = {Computer Networks},
volume = {186},
pages = {107735},
publisher = {Elsevier BV},
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 FCA-LoRa leveraging fairness and improving 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 FCA-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 gateway scenario with 600 nodes, FCA-LoRa can increase throughput by nearly 50%while in a multiple gateway scenario, throughput reaches an increase of 49% for 500 nodes. © 2020 Elsevier B.V.},
keywords = {Collision avoidance, Fairness, Internet of things, LoRa, LoRaWAN, Low-Power Wide Area Networks, Medium Access Control, Scalability},
pubstate = {published},
tppubtype = {article}
}
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 FCA-LoRa leveraging fairness and improving 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 FCA-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 gateway scenario with 600 nodes, FCA-LoRa can increase throughput by nearly 50%while in a multiple gateway scenario, throughput reaches an increase of 49% for 500 nodes. © 2020 Elsevier B.V.
Address
Internet of Things and Applications Lab
Department of Electrical and Computer Engineering
University of Western Macedonia Campus
ZEP Area, Kozani 50100
Greece
Contact Information
tel: +30 2461 056527
Email: ithaca@uowm.gr
