Nacereddine Sitouah

Nacereddine Sitouah, Fatiha Merazka, Abdenour Hedjazi

The developments of satellite communication in network systems require strong and effective security plans. Attacks such as denial of service (DoS) can be detected through the use of machine learning techniques, especially under normal operational conditions. This work aims to provide an interruption detection strategy for Low Earth Orbit (\textsf{LEO}) satellite networks using deep learning algorithms. Both the training, and the testing of the proposed models are carried out with our own communication datasets, created by utilizing a satellite traffic (benign and malicious) that was generated using satellite networks simulation platforms, Omnet++ and Inet. We test different deep learning algorithms including Multi Layer Perceptron (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Gated Recurrent Units (GRU), and Long Short-term Memory (LSTM). Followed by a full analysis and investigation of detection rate in both binary classification, and multi-classes classification that includes different interruption categories such as Distributed DoS (DDoS), Network Jamming, and meteorological disturbances. Simulation results for both classification types surpassed 99.33% in terms of detection rate in scenarios of full network surveillance. However, in more realistic scenarios, the best-recorded performance was 96.12% for the detection of binary traffic and 94.35% for the detection of multi-class traffic with a false positive rate of 3.72%, using a hybrid model that combines MLP and GRU. This Deep Learning approach efficiency calls for the necessity of using machine learning methods to improve security and to give more awareness to search for solutions that facilitate data collection in LEO satellite networks.

Nacereddine Sitouah, Marco Esposito, Francesco Bruschi

European digital identity initiatives are grounded in regulatory frameworks designed to ensure interoperability and robust, harmonized security standards. The evolution of these frameworks culminates in eIDAS 2.0, whose origins trace back to the Electronic Signatures Directive 1999/93/EC, the first EU-wide legal foundation for the use of electronic signatures in cross-border electronic transactions. As technological capabilities advanced, the initial eIDAS 1.0 framework was increasingly criticized for its limitations and lack of comprehensiveness. Emerging decentralized approaches further exposed these shortcomings and introduced the possibility of integrating innovative identity paradigms, such as Self-Sovereign Identity (SSI) models. In this article, we contribute to the ongoing legal and policy debate on the European Digital Identity Framework by analyzing key provisions of eIDAS 2.0 and its accompanying recitals, drawing on a systematic literature review guided by defined Research Questions (RQ). This work employs a structured methodological approach that combines descriptive and comparative analysis, systematic gap analysis supported by a defined scoring matrix, and normative analysis to evaluate the compatibility of SSI properties with eIDAS 2.0 regulation, as operationalized via its Architecture and Reference Framework (ARF). Furthermore, we assess the ARF's guidelines and examine the extent to which it aligns with SSI. The analysis adopts a complementary perspective demonstrating how the regulation can be further developed to better support SSI in the future by identifying existing limitations and potential adoption opportunities within the current legal foundations of the framework.