5 papers · 1 filter
TemporalFED: Detecting Cyberattacks in Industrial Time-Series Data Using Decentralized Federated Learning
Ãngel Luis Perales Gómez, Enrique Tomás MartÃnez Beltrán, Pedro Miguel Sánchez Sánchez +1
Industry 4.0 has brought numerous advantages, such as increasing productivity through automation. However, it also presents major cybersecurity issues such as cyberattacks affectin…
RepuNet: A Reputation System for Mitigating Malicious Clients in DFL
Isaac Marroqui Penalva, Enrique Tomás MartÃnez Beltrán, Manuel Gil Pérez +1
Decentralized Federated Learning (DFL) enables nodes to collaboratively train models without a central server, introducing new vulnerabilities since each node independently selects…
Leveraging MTD to Mitigate Poisoning Attacks in Decentralized FL with Non-IID Data
Chao Feng, Alberto Huertas Celdrán, Zien Zeng +4
Decentralized Federated Learning (DFL), a paradigm for managing big data in a privacy-preserved manner, is still vulnerable to poisoning attacks where malicious clients tamper with…
CyberForce: A Federated Reinforcement Learning Framework for Malware Mitigation
Chao Feng, Alberto Huertas Celdran, Pedro Miguel Sanchez Sanchez +5
Recent research has shown that the integration of Reinforcement Learning (RL) with Moving Target Defense (MTD) can enhance cybersecurity in Internet-of-Things (IoT) devices. Nevert…
Transfer Learning in Pre-Trained Large Language Models for Malware Detection Based on System Calls
Pedro Miguel Sánchez Sánchez, Alberto Huertas Celdrán, Gérôme Bovet +1
In the current cybersecurity landscape, protecting military devices such as communication and battlefield management systems against sophisticated cyber attacks is crucial. Malware…