@article{articledb,
title = {Strategic Honeypot Deployment in Ultra-Dense Beyond 5G Networks: A Reinforcement Learning Approach},
author = {Panagiotis Radoglou Grammatikis and Panagiotis Sarigiannidis and Panagiotis Diamantoulakis and Thomas Lagkas and Theocharis Saoulidis and Eleftherios Fountoukidis and George Karagiannidis},
url = {https://www.researchgate.net/publication/361139812_Strategic_Honeypot_Deployment_in_Ultra-Dense_Beyond_5G_Networks_A_Reinforcement_Learning_Approach},
doi = {10.1109/TETC.2022.3184112},
issn = {2168-6750},
year = {2022},
date = {2022-06-01},
urldate = {2022-01-01},
journal = {IEEE Transactions on Emerging Topics in Computing},
abstract = {The progression of Software Defined Networking (SDN) and the virtualisation technologies lead to the beyond 5G era, providing multiple benefits in the smart economies. However, despite the advantages, security issues still remain. In particular, SDN/NFV and cloud/edge computing are related to various security issues. Moreover, due to the wireless nature of the entities, they are prone to a wide range of cyberthreats. Therefore, the presence of appropriate intrusion detection mechanisms is critical. Although both Machine Learning (ML) and Deep Learning (DL) have optimised the typical rule-based detection systems, the use of ML and DL requires labelled pre-existing datasets. However, this kind of data varies based on the nature of the respective environment. Another smart solution for detecting intrusions is to use honeypots. A honeypot acts as a decoy with the goal to mislead the cyberatatcker and protect the real assets. In this paper, we focus on Wireless Honeypots (WHs) in ultradense networks. In particular, we introduce a strategic honeypot deployment method, using two Reinforcement Learning (RL) techniques: (a) e−Greedy and (b) Q−Learning. Both methods aim to identify the optimal number of honeypots that can be deployed for protecting the actual entities. The experimental results demonstrate the efficacy of both methods.},
keywords = {Honeypot, Intrusion detection, ReinforcementLearning, Wireless communication},
pubstate = {published},
tppubtype = {article}
}

@article{unknown,
title = {Threatening the 5G Core via PFCP DoS Attacks: The Case of Blocking UAV Communications},
author = {Georgios Amponis and Panagiotis Radoglou Grammatikis and Thomas Lagkas and W Mallouli and Ana Cavalli and Dimitris Klonidis and Evangelos Markakis and Panagiotis Sarigiannidis},
url = {https://www.researchgate.net/publication/361231451_Threatening_the_5G_Core_via_PFCP_DoS_Attacks_The_Case_of_Blocking_UAV_Communications},
doi = {10.21203/rs.3.rs-1708948/v1},
year = {2022},
date = {2022-05-01},
abstract = {The modern communications landscape requires reliable, high-speed, high- throughput and secure links and sessions between user equipment instances and the data network. The 5G core implements the newly defined 3GPP network ar- chitecture enabling faster connectivity, low latency, higher bit rates and network reliability. The full potential of this set of networks will support a set of critical Internet of Things (IoT) and industrial use cases. Nevertheless, several compo- nents and interfaces of the Next-Generation Radio Access Network (NG-RAN) have proven to be vulnerable to attacks that can potentially obstruct the net- work’s capability to provide reliable end-to-end communication services. Various inherent security flaws and protocol-specific weaknesses have also been identified within the 5G core itself. However, little to no research has gone into testing and exposing said core-related weaknesses, contrary to those concerning the NG-RAN. In this paper, we investigate, describe, develop, implement and finally test a set of attacks on the Packet Forwarding Control Protocol (PFCP) inside the 5G core. We find that, by transmitting unauthorised session control packets, we were able to disrupt established 5G tunnels without disrupting subscribers’ connectivity to the NG-RAN, thus hindering the detection of said attacks. We evaluate the identi- fied PFCP attacks in a drone-based scenario involving 5G tunnelling between two swarms.},
keywords = {5G Security, 5G Testbed, DoS Attacks, PFCP, UAV Communications},
pubstate = {published},
tppubtype = {article}
}

@article{electronics11060965,
title = {Data Protection and Cybersecurity Certification Activities and Schemes in the Energy Sector},
author = { M. Stauch P. Radoglou-Grammatikis P. Sarigiannidis G. Lazaridis A. Drosou I. Nwankwo and D. Tzovaras},
url = {https://www.researchgate.net/publication/359370929_Data_Protection_and_Cybersecurity_Certification_Activities_and_Schemes_in_the_Energy_Sector},
doi = {10.3390/electronics11060965},
issn = {2079-9292},
year = {2022},
date = {2022-02-12},
journal = {Electronics},
volume = {11},
number = {6},
abstract = {Cybersecurity concerns have been at the forefront of regulatory reform in the European Union (EU) recently. One of the outcomes of these reforms is the introduction of certification schemes for information and communication technology (ICT) products, services and processes, as well as for data processing operations concerning personal data. These schemes aim to provide an avenue for consumers to assess the compliance posture of organisations concerning the privacy and security of ICT products, services and processes. They also present manufacturers, providers and data controllers with the opportunity to demonstrate compliance with regulatory requirements through a verifiable third-party assessment. As these certification schemes are being developed, various sectors, including the electrical power and energy sector, will need to access the impact on their operations and plan towards successful implementation. Relying on a doctrinal method, this paper identifies relevant EU legal instruments on data protection and cybersecurity certification and their interpretation in order to examine their potential impact when applying certification schemes within the Electrical Power and Energy System (EPES) domain. The result suggests that the EPES domain employs different technologies and services from diverse areas, which can result in the application of several certification schemes within its environment, including horizontal, technological and sector-specific schemes. This has the potential for creating a complex constellation of implementation models and would require careful design to avoid proliferation and disincentivising of stakeholders.},
keywords = {certification, Cybersecurity, data protection, energy},
pubstate = {published},
tppubtype = {article}
}

@article{article,
title = {Detection and Characterization of Stressed Sweet Cherry Tissues Using Machine Learning},
author = { C. Chaschatzis and C. Karaiskou and E. Mouratidis and E. Karagiannis and P. Sarigiannidis},
url = {https://www.researchgate.net/publication/357257849_Detection_and_Characterization_of_Stressed_Sweet_Cherry_Tissues_Using_Machine_Learning},
doi = {10.3390/drones6010003},
year = {2021},
date = {2021-12-22},
journal = {Drones},
volume = {6},
pages = {3},
abstract = {Recent technological developments in the primary sector and machine learning algorithms allow the combined application of many promising solutions in precision agriculture. For example, the YOLOv5 (You Only Look Once) and ResNet Deep Learning architecture provide high-precision real-time identifications of objects. The advent of datasets from different perspectives provides multiple benefits, such as spheric view of objects, increased information, and inference results from multiple objects detection per image. However, it also raises crucial obstacles such as total identifications (ground truths) and processing concerns that can lead to devastating consequences, including false-positive detections with other erroneous conclusions or even the inability to extract results. This paper introduces experimental results from the machine learning algorithm (Yolov5) on a novel dataset based on perennial fruit crops, such as sweet cherries, aiming to enhance precision agriculture resiliency. Detection is oriented on two points of interest: (a) Infected leaves and (b) Infected branches. It is noteworthy that infected leaves or branches indicate stress, which may be due to either a stress/disease (e.g., Armillaria for sweet cherries trees, etc.) or other factors (e.g., water shortage, etc). Correspondingly, the foliage of a tree shows symptoms, while this indicates the stages of the disease.},
keywords = {diseases detection, machine learning, precision agriculture, ResNet, smart farming, stress detection, sweet cherries trees, Yolov5},
pubstate = {published},
tppubtype = {article}
}

@article{Pliatsios2021b,
title = {Drone-Base-Station for Next-Generation Internet-of-Things: A Comparison of Swarm Intelligence Approaches},
author = {Dimitrios Pliatsios and Sotirios K. Goudos and Thomas Lagkas and Vasileios Argyriou and Alexandros Apostolos A. Boulogeorgos and Panagiotis Sarigiannidis},
url = {https://www.researchgate.net/publication/356863442_Drone-Base-Station_for_Next-Generation_Internet-of-Things_A_Comparison_of_Swarm_Intelligence_Approaches},
doi = {10.1109/OJAP.2021.3133459},
issn = {2637-6431},
year = {2021},
date = {2021-12-07},
journal = {IEEE Open Journal of Antennas and Propagation},
abstract = {The emergence of next-generation internet-of-things (NG-IoT) applications introduces several challenges for the sixth-generation (6G) mobile networks, such as massive connectivity, increased network capacity, and extremely low-latency. To countermeasure the aforementioned challenges, ultra-dense networking has been widely identified as a possible solution. However, the dense deployment of base stations (BSs) is not always possible or cost-efficient. Drone-base-stations (DBSs) can facilitate network expansion and efficiently address the requirements of NG-IoT. In addition, due to their flexibility, they can provide on-demand connectivity in emergency scenarios or address temporary increases in network traffic. Nevertheless, the optimal placement of a DBS is not a straightforward task due to the limited energy reserves and the increased signal quality degradation in air-to-ground links. To this end, swarm intelligence approaches can be attractive solutions for determining the optimal position of the DBS in the three-dimensional (3D) space. In this work, we explore well-known swarm intelligence approaches, namely the Cuckoo Search (CS), Elephant Herd Optimization (EHO), Grey Wolf Optimization (GWO), Monarch Butterfly Optimization (MBO), Salp Swarm Algorithm (SSA), and Particle Swarm Optimization (PSO) and investigate their performance and efficiency in solving the aforementioned problem. In particular, we investigate the performance of three scenarios in the presence of different swarm intelligence approaches. Additionally, we carry out non-parametric statistical tests, namely the Friedman and Wilcoxon tests, in order to compare the different approaches.},
keywords = {Drone base station, evolutionary algorithms, mobile communications, Optimization methods, Swarm intelligence},
pubstate = {published},
tppubtype = {article}
}

@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}
}