@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}

}

@conference{Grammatikis2020,

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

author = { P.R. Grammatikis and P. Sarigiannidis and A. Sarigiannidis and D. Margounakis and A. Tsiakalos and G. 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},

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

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. © 2020 IEEE.},

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

pubstate = {published},

tppubtype = {conference}

}

@conference{Protopsaltis2020,

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

author = { A. Protopsaltis and P. Sarigiannidis and D. Margounakis and A. Lytos},

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

doi = {10.1145/3407023.3409228},

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 = {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 = {conference}

}

@conference{Radoglou-Grammatikis2020b,

title = {DIDEROT: An intrusion detection and prevention system for DNP3-based SCADA systems},

author = { P. Radoglou-Grammatikis and P. Sarigiannidis and G. Efstathopoulos and P.-A. Karypidis and A. Sarigiannidis},

url = {https://www.researchgate.net/publication/343853580_DIDEROT_an_intrusion_detection_and_prevention_system_for_DNP3-based_SCADA_systems},

doi = {10.1145/3407023.3409314},

year  = {2020},

date = {2020-01-01},

journal = {ACM International Conference Proceeding Series},

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. © 2020 ACM.},

keywords = {Anomaly Detection, Autonencoder, Intrusion detection, machine learning, SCADA, SDN, Smart Grid},

pubstate = {published},

tppubtype = {conference}

}

@conference{Efstathopoulos2019,

title = {Operational data based intrusion detection system for smart grid},

author = { G. Efstathopoulos and P.R. Grammatikis and P. Sarigiannidis and V. Argyriou and A. Sarigiannidis and K. Stamatakis and M.K. Angelopoulos and S.K. Athanasopoulos},

url = {https://www.researchgate.net/publication/335866997_Operational_Data_Based_Intrusion_Detection_System_for_Smart_Grid},

doi = {10.1109/CAMAD.2019.8858503},

year  = {2019},

date = {2019-01-01},

journal = {IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD},

volume = {2019-September},

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. © 2019 IEEE.},

keywords = {Anomaly Detection, Cybersecurity, intrusion detection system, machine learning, Operational Data, Smart Grid},

pubstate = {published},

tppubtype = {conference}

}