Publications
PUBLICATIONS
2021 |
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1. | Kelli, Vasiliki; Argyriou, Vasileios; Lagkas, Thomas; Fragulis, George; Grigoriou, Elisavet; Sarigiannidis, Panagiotis IDS for industrial applications: a federated learning approach with active personalization (Journal Article) In: Sensors, vol. 21, no. 20, pp. 6743, 2021. (Abstract | Links | BibTeX | Tags: Active Learning, Federated Learning, Internet of Things (IoT), Intrusion Detection System, Machine Learning, Personalization) @article{kelli2021ids, 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 |
2019 |
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2. | Efstathopoulos, Georgios; Grammatikis, Panagiotis Radoglou; Sarigiannidis, Panagiotis; Argyriou, Vasilis; Sarigiannidis, Antonios; Stamatakis, Konstantinos; Angelopoulos, Michail K; Athanasopoulos, Solon K Operational Data Based Intrusion Detection System for Smart Grid (Inproceedings) In: 2019 IEEE 24th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), pp. 1-6, 2019. (Abstract | Links | BibTeX | Tags: Anomaly Detection, Cybersecurity, Intrusion Detection System, Machine Learning, Operational Data, Smart Grid) @inproceedings{8858503, 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. |