most citedStudying the Robustness of Anti-adversarial Federated Learning Models Detecting Cyberattacks in IoT Spectrum Sensors

31 citations · 52 across the 2 of their papers we have counts for

collaborators

2 papers

cs.CR2022★ 21 cited

RL and Fingerprinting to Select Moving Target Defense Mechanisms for Zero-day Attacks in IoT

Alberto Huertas Celdrán, Pedro Miguel Sánchez Sánchez, Jan von der Assen +4

Cybercriminals are moving towards zero-day attacks affecting resource-constrained devices such as single-board computers (SBC). Assuming that perfect security is unrealistic, Movin…

cs.CR2022★ 31 cited

Studying the Robustness of Anti-adversarial Federated Learning Models Detecting Cyberattacks in IoT Spectrum Sensors

Pedro Miguel Sánchez Sánchez, Alberto Huertas Celdrán, Timo Schenk +4

Device fingerprinting combined with Machine and Deep Learning (ML/DL) report promising performance when detecting cyberattacks targeting data managed by resource-constrained spectr…