31 citations · 145 across the 16 of their papers we have counts for
5 papers · 1 filter
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…
Analyzing the Robustness of Decentralized Horizontal and Vertical Federated Learning Architectures in a Non-IID Scenario
Pedro Miguel Sánchez Sánchez, Alberto Huertas Celdrán, Enrique Tomás Martínez Beltrán +4
Federated learning (FL) allows participants to collaboratively train machine and deep learning models while protecting data privacy. However, the FL paradigm still presents drawbac…
A Lightweight Moving Target Defense Framework for Multi-purpose Malware Affecting IoT Devices
Jan von der Assen, Alberto Huertas Celdrán, Pedro Miguel Sánchez Sánchez +4
Malware affecting Internet of Things (IoT) devices is rapidly growing due to the relevance of this paradigm in real-world scenarios. Specialized literature has also detected a tren…
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…
CyberSpec: Intelligent Behavioral Fingerprinting to Detect Attacks on Crowdsensing Spectrum Sensors
Alberto Huertas Celdrán, Pedro Miguel Sánchez Sánchez, Gérôme Bovet +2
Integrated sensing and communication (ISAC) is a novel paradigm using crowdsensing spectrum sensors to help with the management of spectrum scarcity. However, well-known vulnerabil…