69 citations · 88 across the 13 of their papers we have counts for
13 papers
Empowering IoT Security: On-Device Intrusion Detection in Resource Constrained Devices
Vasilis Ieropoulos, Eirini Anthi, Theodoros Spyridopoulos +3
IoT devices particularly microcontrollers are challenged by their inherent limitations in processing capabilities, memory capacity, and energy conservation. Securing communication…
Gotham Dataset 2025: A Reproducible Large-Scale IoT Network Dataset for Intrusion Detection and Security Research
Othmane Belarbi, Theodoros Spyridopoulos, Eirini Anthi +3
In this paper, a dataset of IoT network traffic is presented. Our dataset was generated by utilising the Gotham testbed, an emulated large-scale Internet of Things (IoT) network de…
Multi-stage Attack Detection and Prediction Using Graph Neural Networks: An IoT Feasibility Study
Hamdi Friji, Ioannis Mavromatis, Adrian Sanchez-Mompo +3
With the ever-increasing reliance on digital networks for various aspects of modern life, ensuring their security has become a critical challenge. Intrusion Detection Systems play…
Cybersecurity in Motion: A Survey of Challenges and Requirements for Future Test Facilities of CAVs
Ioannis Mavromatis, Theodoros Spyridopoulos, Pietro Carnelli +12
The way we travel is changing rapidly, and Cooperative Intelligent Transportation Systems (C-ITSs) are at the forefront of this evolution. However, the adoption of C-ITSs introduce…
FROST: Towards Energy-efficient AI-on-5G Platforms -- A GPU Power Capping Evaluation
Ioannis Mavromatis, Stefano De Feo, Pietro Carnelli +2
The Open Radio Access Network (O-RAN) is a burgeoning market with projected growth in the upcoming years. RAN has the highest CAPEX impact on the network and, most importantly, con…
Federated Deep Learning for Intrusion Detection in IoT Networks
Othmane Belarbi, Theodoros Spyridopoulos, Eirini Anthi +3
The vast increase of Internet of Things (IoT) technologies and the ever-evolving attack vectors have increased cyber-security risks dramatically. A common approach to implementing…
FLARE: Detection and Mitigation of Concept Drift for Federated Learning based IoT Deployments
Theo Chow, Usman Raza, Ioannis Mavromatis +1
Intelligent, large-scale IoT ecosystems have become possible due to recent advancements in sensing technologies, distributed learning, and low-power inference in embedded devices.…
Hierarchical and Decentralised Federated Learning
Omer Rana, Theodoros Spyridopoulos, Nathaniel Hudson +4
Federated learning has shown enormous promise as a way of training ML models in distributed environments while reducing communication costs and protecting data privacy. However, th…
Toward Multi-Service Edge-Intelligence Paradigm: Temporal-Adaptive Prediction for Time-Critical Control over Wireless
Adnan Aijaz, Nan Jiang, Aftab Khan
Time-critical control applications typically pose stringent connectivity requirements for communication networks. The imperfections associated with the wireless medium such as pack…
LE3D: A Lightweight Ensemble Framework of Data Drift Detectors for Resource-Constrained Devices
Ioannis Mavromatis, Adrian Sanchez-Mompo, Francesco Raimondo +8
Data integrity becomes paramount as the number of Internet of Things (IoT) sensor deployments increases. Sensor data can be altered by benign causes or malicious actions. Mechanism…
Demo: LE3D: A Privacy-preserving Lightweight Data Drift Detection Framework
Ioannis Mavromatis, Aftab Khan
This paper presents LE3D; a novel data drift detection framework for preserving data integrity and confidentiality. LE3D is a generalisable platform for evaluating novel drift dete…
A Federated Learning-enabled Smart Street Light Monitoring Application: Benefits and Future Challenges
Diya Anand, Ioannis Mavromatis, Pietro Carnelli +1
Data-enabled cities are recently accelerated and enhanced with automated learning for improved Smart Cities applications. In the context of an Internet of Things (IoT) ecosystem, t…
An Intrusion Detection System based on Deep Belief Networks
Othmane Belarbi, Aftab Khan, Pietro Carnelli +1
The rapid growth of connected devices has led to the proliferation of novel cyber-security threats known as zero-day attacks. Traditional behaviour-based IDS rely on DNN to detect…