@article{spyridis2021towards,

title = {Towards 6G IoT: Tracing mobile sensor nodes with deep learning clustering in UAV networks},

author = {Yannis Spyridis and Thomas Lagkas and Panagiotis Sarigiannidis and Vasileios Argyriou and Antonios Sarigiannidis and George Eleftherakis and Jie Zhang},

url = {https://www.mdpi.com/1424-8220/21/11/3936},

doi = {10.3390/s21113936},

year  = {2021},

date = {2021-06-07},

urldate = {2021-01-01},

journal = {Sensors},

volume = {21},

number = {11},

pages = {3936},

publisher = {MDPI},

abstract = {Unmanned aerial vehicles (UAVs) in the role of flying anchor nodes have been proposed to assist the localisation of terrestrial Internet of Things (IoT) sensors and provide relay services in the context of the upcoming 6G networks. This paper considered the objective of tracing a mobile IoT device of unknown location, using a group of UAVs that were equipped with received signal strength indicator (RSSI) sensors. The UAVs employed measurements of the target’s radio frequency (RF) signal power to approach the target as quickly as possible. A deep learning model performed clustering in the UAV network at regular intervals, based on a graph convolutional network (GCN) architecture, which utilised information about the RSSI and the UAV positions. The number of clusters was determined dynamically at each instant using a heuristic method, and the partitions were determined by optimising an RSSI loss function. The proposed algorithm retained the clusters that approached the RF source more effectively, removing the rest of the UAVs, which returned to the base. Simulation experiments demonstrated the improvement of this method compared to a previous deterministic approach, in terms of the time required to reach the target and the total distance covered by the UAVs.},

keywords = {6G, Deep Learning, Internet of Things (IoT), Sensors, Unmanned aerial vehicles},

pubstate = {published},

tppubtype = {article}

}

@article{kelli2021ids,

title = {IDS for industrial applications: a federated learning approach with active personalization},

author = {Vasiliki Kelli and Vasileios Argyriou and Thomas Lagkas and George Fragulis and Elisavet Grigoriou and Panagiotis Sarigiannidis},

url = {https://www.mdpi.com/1424-8220/21/20/6743},

doi = {10.3390/s21206743},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {Sensors},

volume = {21},

number = {20},

pages = {6743},

publisher = {MDPI},

abstract = { Internet of Things (IoT) is a concept adopted in nearly every aspect of human life, leading to an explosive utilization of intelligent devices. Notably, such solutions are especially integrated in the industrial sector, to allow the remote monitoring and control of critical infrastructure. Such global integration of IoT solutions has led to an expanded attack surface against IoT-enabled infrastructures. Artificial intelligence and machine learning have demonstrated their ability to resolve issues that would have been impossible or difficult to address otherwise; thus, such solutions are closely associated with securing IoT. Classical collaborative and distributed machine learning approaches are known to compromise sensitive information. In our paper, we demonstrate the creation of a network flow-based Intrusion Detection System (IDS) aiming to protecting critical infrastructures, stemming from the pairing of two machine learning techniques, namely, federated learning and active learning. The former is utilized for privately training models in federation, while the latter is a semi-supervised approach applied for global model adaptation to each of the participant’s traffic. Experimental results indicate that global models perform significantly better for each participant, when locally personalized with just a few active learning queries. Specifically, we demonstrate how the accuracy increase can reach 7.07% in only 10 queries. View Full-Text },

keywords = {Active Learning, Federated Learning, Internet of Things (IoT), Intrusion Detection System, Machine Learning, Personalization},

pubstate = {published},

tppubtype = {article}

}

@article{radoglou2021modeling,

title = {Modeling, detecting, and mitigating threats against industrial healthcare systems: a combined software defined networking and reinforcement learning approach},

author = {Panagiotis Radoglou-Grammatikis and Konstantinos Rompolos and Panagiotis Sarigiannidis and Vasileios Argyriou and Thomas Lagkas and Antonios Sarigiannidis and Sotirios Goudos and Shaohua Wan},

url = {https://ieeexplore.ieee.org/abstract/document/9470933},

doi = {10.1109/TII.2021.3093905},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {IEEE Transactions on Industrial Informatics},

volume = {18},

number = {3},

pages = {2041--2052},

publisher = {IEEE},

abstract = {The rise of the Internet of Medical Things introduces the healthcare ecosystem in a new digital era with multiple benefits, such as remote medical assistance, real-time monitoring, and pervasive control. However, despite the valuable healthcare services, this progression raises significant cybersecurity and privacy concerns. In this article, we focus our attention on the IEC 60 870-5-104 protocol, which is widely adopted in industrial healthcare systems. First, we investigate and assess the severity of the IEC 60 870-5-104 cyberattacks by providing a quantitative threat model, which relies on Attack Defence Trees and Common Vulnerability Scoring System v3.1. Next, we introduce an intrusion detection and prevention system (IDPS), which is capable of discriminating and mitigating automatically the IEC 60 870-5-104 cyberattacks. The proposed IDPS takes full advantage of the machine learning (ML) and software defined networking (SDN) technologies. ML is used to detect the IEC 60 870-5-104 cyberattacks, utilizing 1) Transmission Control Protocol/Internet Protocol network flow statistics and 2) IEC 60 870-5-104 payload flow statistics. On the other side, the automated mitigation is transformed into a multiarmed bandit problem, which is solved through a reinforcement learning method called Thomson sampling and SDN. The evaluation analysis demonstrates the efficiency of the proposed IDPS in terms of intrusion detection accuracy and automated mitigation performance. The detection accuracy and the F1 score of the proposed IDPS reach 0.831 and 0.8258, respectively, while the mitigation accuracy is calculated at 0.923.},

keywords = {Cybersecurity, IEC-60870- 5-104, Internet of Things (IoT), Intrusion detection, Machine Learning, Software Defined Networks},

pubstate = {published},

tppubtype = {article}

}