581 citations · 974 across the 36 of their papers we have counts for
20 papers · 1 filter
A Crowdsensing Intrusion Detection Dataset For Decentralized Federated Learning Models
Chao Feng, Alberto Huertas Celdran, Jing Han +6
This paper introduces a dataset and an experimental study on Decentralized Federated Learning (DFL) for Internet of Things (IoT) crowdsensing malware detection. The dataset compris…
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…
Asset-centric Threat Modeling for AI-based Systems
Jan von der Assen, Jamo Sharif, Chao Feng +3
Threat modeling is a popular method to securely develop systems by achieving awareness of potential areas of future damage caused by adversaries. However, threat modeling for syste…
GuardFS: a File System for Integrated Detection and Mitigation of Linux-based Ransomware
Jan von der Assen, Chao Feng, Alberto Huertas Celdrán +3
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…
Voyager: MTD-Based Aggregation Protocol for Mitigating Poisoning Attacks on DFL
Chao Feng, Alberto Huertas Celdran, Michael Vuong +2
The growing concern over malicious attacks targeting the robustness of both Centralized and Decentralized Federated Learning (FL) necessitates novel defensive strategies. In contra…
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…
Mitigating Communications Threats in Decentralized Federated Learning through Moving Target Defense
Enrique Tomás Martínez Beltrán, Pedro Miguel Sánchez Sánchez, Sergio López Bernal +4
The rise of Decentralized Federated Learning (DFL) has enabled the training of machine learning models across federated participants, fostering decentralized model aggregation and…
RansomAI: AI-powered Ransomware for Stealthy Encryption
Jan von der Assen, Alberto Huertas Celdrán, Janik Luechinger +4
Cybersecurity solutions have shown promising performance when detecting ransomware samples that use fixed algorithms and encryption rates. However, due to the current explosion of…
MTFS: a Moving Target Defense-Enabled File System for Malware Mitigation
Jan von der Assen, Alberto Huertas Celdrán, Rinor Sefa +2
Ransomware has remained one of the most notorious threats in the cybersecurity field. Moving Target Defense (MTD) has been proposed as a novel paradigm for proactive defense. Altho…
Single-board Device Individual Authentication based on Hardware Performance and Autoencoder Transformer Models
Pedro Miguel Sánchez Sánchez, Alberto Huertas Celdrán, Gérôme Bovet +1
The proliferation of the Internet of Things (IoT) has led to the emergence of crowdsensing applications, where a multitude of interconnected devices collaboratively collect and ana…
FederatedTrust: A Solution for Trustworthy Federated Learning
Pedro Miguel Sánchez Sánchez, Alberto Huertas Celdrán, Ning Xie +3
The rapid expansion of the Internet of Things (IoT) and Edge Computing has presented challenges for centralized Machine and Deep Learning (ML/DL) methods due to the presence of dis…
Adversarial attacks and defenses on ML- and hardware-based IoT device fingerprinting and identification
Pedro Miguel Sánchez Sánchez, Alberto Huertas Celdrán, Gérôme Bovet +1
In the last years, the number of IoT devices deployed has suffered an undoubted explosion, reaching the scale of billions. However, some new cybersecurity issues have appeared toge…
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…
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…
Robust Federated Learning for execution time-based device model identification under label-flipping attack
Pedro Miguel Sánchez Sánchez, Alberto Huertas Celdrán, José Rafael Buendía Rubio +2
The computing device deployment explosion experienced in recent years, motivated by the advances of technologies such as Internet-of-Things (IoT) and 5G, has led to a global scenar…
A methodology to identify identical single-board computers based on hardware behavior fingerprinting
Pedro Miguel Sánchez Sánchez, José María Jorquera Valero, Alberto Huertas Celdrán +3
The connectivity and resource-constrained nature of single-board devices open the door to cybersecurity concerns affecting Internet of Things (IoT) scenarios. One of the most impor…
Federated Learning for Malware Detection in IoT Devices
Valerian Rey, Pedro Miguel Sánchez Sánchez, Alberto Huertas Celdrán +2
This work investigates the possibilities enabled by federated learning concerning IoT malware detection and studies security issues inherent to this new learning paradigm. In this…
A Survey on Device Behavior Fingerprinting: Data Sources, Techniques, Application Scenarios, and Datasets
Pedro Miguel Sánchez Sánchez, Jose María Jorquera Valero, Alberto Huertas Celdrán +3
In the current network-based computing world, where the number of interconnected devices grows exponentially, their diversity, malfunctions, and cybersecurity threats are increasin…