EUVD-2023-32833

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EUVD-2023-50410

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EUVD-2025-0153

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EUVD-2025-21766

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EUVD-2021-28813

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EUVD-2023-0255

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EUVD-2024-0169

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EUVD-2023-34860

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EUVD-2024-0423

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EUVD-2025-7668

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EUVD-2024-0168

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EUVD-2024-0829

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EUVD-2022-52754

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EUVD-2023-0258

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A Lightweight Federated Learning Approach for Privacy-Preserving Botnet Detection in IoT

The rapid growth of the Internet of Things IoT has expanded opportunities for innovation but also increased exposure to botnet-driven cyberattacks. Conventional detection methods often struggle with scalability, privacy, and adaptability in resource-constrained IoT environments. To address these...

Federated Spatiotemporal Graph Learning for Passive Attack Detection in Smart Grids

Smart grids are exposed to passive eavesdropping, where attackers listen silently to communication links. Although no data is actively altered, such reconnaissance can reveal grid topology, consumption patterns, and operational behavior, creating a gateway to more severe targeted attacks. Detecti...

AntiFLipper: A Secure and Efficient Defense against Label-Flipping Attacks in Federated Learning

Federated learning FL enables privacy-preserving model training by keeping data decentralized. However, it remains vulnerable to label-flipping attacks, where malicious clients manipulate labels to poison the global model. Despite their simplicity, these attacks can severely degrade model...

Can Federated Learning Safeguard Private Data in LLM Training? Vulnerabilities, Attacks, and Defense Evaluation

Fine-tuning large language models LLMs with local data is a widely adopted approach for organizations seeking to adapt LLMs to their specific domains. Given the shared characteristics in data across different organizations, the idea of collaboratively fine-tuning an LLM using data from multiple...

Centralized Vs. Decentralized Security for Space AI Systems? A New Look

This paper investigates the trade-off between centralized and decentralized security management in constellations of satellites to balance security and performance. We highlight three key AI architectures for automated security management: a centralized, b distributed and c federated. The...

Towards Adapting Federated and Quantum Machine Learning for Network Intrusion Detection: a Survey

This survey explores the integration of Federated Learning FL with Network Intrusion Detection Systems NIDS, with particular emphasis on deep learning and quantum machine learning approaches. FL enables collaborative model training across distributed devices while preserving data privacy-a critic...