Zobrazeno 1 - 10
of 830
pro vyhledávání: '"A, Celdran"'
Autor:
Feng, Chao, Celdrán, Alberto Huertas, von der Assen, Jan, Beltrán, Enrique Tomás Martínez, Bovet, Gérôme, Stiller, Burkhard
Federated Learning (FL) has emerged as a promising approach to address privacy concerns inherent in Machine Learning (ML) practices. However, conventional FL methods, particularly those following the Centralized FL (CFL) paradigm, utilize a central s
Externí odkaz:
http://arxiv.org/abs/2407.08652
Autor:
Sánchez, Pedro Miguel Sánchez, Celdrán, Alberto Huertas, Bovet, Gérôme, Pérez, Gregorio Martínez
In the current cybersecurity landscape, protecting military devices such as communication and battlefield management systems against sophisticated cyber attacks is crucial. Malware exploits vulnerabilities through stealth methods, often evading tradi
Externí odkaz:
http://arxiv.org/abs/2405.09318
Autor:
von der Assen, Jan, Feng, Chao, Celdrán, Alberto Huertas, Oleš, Róbert, Bovet, Gérôme, Stiller, Burkhard
Although ransomware has received broad attention in media and research, this evolving threat vector still poses a systematic threat. Related literature has explored their detection using various approaches leveraging Machine and Deep Learning. While
Externí odkaz:
http://arxiv.org/abs/2401.17917
Autor:
Bernal, Eduardo López, Bernal, Sergio López, Pérez, Gregorio Martínez, Celdrán, Alberto Huertas
Brain-Computer Interfaces (BCIs) are used in various application scenarios allowing direct communication between the brain and computers. Specifically, electroencephalography (EEG) is one of the most common techniques for obtaining evoked potentials
Externí odkaz:
http://arxiv.org/abs/2311.05270
Autor:
Celdran, Alberto Huertas, Feng, Chao, Sanchez, Pedro Miguel Sanchez, Zumtaugwald, Lynn, Bovet, Gerome, Stiller, Burkhard
Artificial intelligence (AI) plays a pivotal role in various sectors, influencing critical decision-making processes in our daily lives. Within the AI landscape, novel AI paradigms, such as Federated Learning (FL), focus on preserving data privacy wh
Externí odkaz:
http://arxiv.org/abs/2310.20435
The growing concern over malicious attacks targeting the robustness of both Centralized and Decentralized Federated Learning (FL) necessitates novel defensive strategies. In contrast to the centralized approach, Decentralized FL (DFL) has the advanta
Externí odkaz:
http://arxiv.org/abs/2310.08739
Autor:
Feng, Chao, Celdrán, Alberto Huertas, Baltensperger, Janosch, Beltrán, Enrique Tomás Martínez, Sánchez, Pedro Miguel Sánchez, Bovet, Gérôme, Stiller, Burkhard
Decentralized Federated Learning (DFL) emerges as an innovative paradigm to train collaborative models, addressing the single point of failure limitation. However, the security and trustworthiness of FL and DFL are compromised by poisoning attacks, n
Externí odkaz:
http://arxiv.org/abs/2310.08097
Autor:
Feng, Chao, Celdran, Alberto Huertas, Sanchez, Pedro Miguel Sanchez, Kreischer, Jan, von der Assen, Jan, Bovet, Gerome, Perez, Gregorio Martinez, Stiller, Burkhard
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. Nevertheless, the practicality of existing work is hindered by data privacy
Externí odkaz:
http://arxiv.org/abs/2308.05978
Autor:
Gómez, Ángel Luis Perales, Beltrán, Enrique Tomás Martínez, Sánchez, Pedro Miguel Sánchez, Celdrán, Alberto Huertas
Industry 4.0 has brought numerous advantages, such as increasing productivity through automation. However, it also presents major cybersecurity issues such as cyberattacks affecting industrial processes. Federated Learning (FL) combined with time-ser
Externí odkaz:
http://arxiv.org/abs/2308.03554
Autor:
Beltrán, Enrique Tomás Martínez, Sánchez, Pedro Miguel Sánchez, Bernal, Sergio López, Bovet, Gérôme, Pérez, Manuel Gil, Pérez, Gregorio Martínez, Celdrán, Alberto Huertas
The rise of Decentralized Federated Learning (DFL) has enabled the training of machine learning models across federated participants, fostering decentralized model aggregation and reducing dependence on a server. However, this approach introduces uni
Externí odkaz:
http://arxiv.org/abs/2307.11730