arXiv subjects

Maria Michalopoulou

Publications and source records attributed to Maria Michalopoulou.

3 recordsLinked to original sources

arXiv2026

A Modular Cyber Range Platform for Smart Energy Systems

Modern critical infrastructure requires realistic training tools that bridge the gap between digital vulnerabilities and physical consequences. This paper introduces a lightweight, modular Cyber Range (CR) designed to simulate Information Technology (IT) and Operational Technology (OT) environments with high fidelity. Using the FastAPI framework as backend and Quick Emulator (QEMU)/Kernel-based Virtual Machine (KVM) virtualisation, the platform employs a YAML-based engine to automatically deploy complex networks on standard hardware with minimal overhead. To demonstrate its capabilities, we present a Photovoltaic (PV) Plant scenario set in a fictional smart city. The scenario follows a multi-stage attack that begins with the compromise of a home Internet of Things (IoT) device and escalates into a major power outage at a military base. The proposed CR, natively supports industrial protocols such as Modbus/TCP and Message Queuing Telemetry Transport (MQTT) and includes a "Green Team" framework for evaluating human performance during crisis situations. This paper describes the system architecture, automated setup, protocol-simulation capabilities, and scenario design.

cs.CR↗

arXiv2026

ACTING: A Platform for Cyber Ranges Federation

Cyber Defence (CD) training requires interoperable cyber-range environments capable of supporting complex, multidomain exercises across distributed infrastructures. This paper presents three main contributions addressing this challenge. First, we introduce the Exercise Description Language - First Generation (EDL-FG), a structured language for formally describing cyber-range training services and exercises. EDL-FG captures both the technical infrastructure required to emulate ICT/OT environments and the scenario logic governing cyber events, injects, and participant interactions, enabling interoperable and automated scenario deployment across federated Cyber Ranges (CRs). Second, the ACTING platform introduces automated PE and scoring mechanisms that assess trainee actions during exercises through coordinated data collection and analysis across participating CRs. Third, the platform enables multi-domain cyber training scenarios that combine civilian and military operational contexts. Building upon federation capabilities established under the H2020 ECHO project, ACTING demonstrates how interoperable scenario description and automated evaluation support scalable and realistic CD training.

cs.CR↗

arXiv2023

Survey on Machine Learning for Traffic-Driven Service Provisioning in Optical Networks

The unprecedented growth of the global Internet traffic, coupled with the large spatio-temporal fluctuations that create, to some extent, predictable tidal traffic conditions, are motivating the evolution from reactive to proactive and eventually towards adaptive optical networks. In these networks, traffic-driven service provisioning can address the problem of network over-provisioning and better adapt to traffic variations, while keeping the quality-of-service at the required levels. Such an approach will reduce network resource over-provisioning and thus reduce the total network cost. This survey provides a comprehensive review of the state of the art on machine learning (ML)-based techniques at the optical layer for traffic-driven service provisioning. The evolution of service provisioning in optical networks is initially presented, followed by an overview of the ML techniques utilized for traffic-driven service provisioning. ML-aided service provisioning approaches are presented in detail, including predictive and prescriptive service provisioning frameworks in proactive and adaptive networks. For all techniques outlined, a discussion on their limitations, research challenges, and potential opportunities is also presented.

cs.NI↗