160 citations · 610 across the 19 of their papers we have counts for
9 papers · 1 filter
Realizable Universal Adversarial Perturbations for Malware
Raphael Labaca-Castro, Luis Muñoz-González, Feargus Pendlebury +3
Machine learning classifiers are vulnerable to adversarial examples -- input-specific perturbations that manipulate models' output. Universal Adversarial Perturbations (UAPs), whic…
Robust Aggregation for Adaptive Privacy Preserving Federated Learning in Healthcare
Matei Grama, Maria Musat, Luis Muñoz-González +3
Federated learning (FL) has enabled training models collaboratively from multiple data owning parties without sharing their data. Given the privacy regulations of patient's healthc…
Efficient attack countermeasure selection accounting for recovery and action costs
Jukka Soikkeli, Luis Muñoz-González, Emil C. Lupu
The losses arising from a system being hit by cyber attacks can be staggeringly high, but defending against such attacks can also be costly. This work proposes an attack countermea…
Procedural Noise Adversarial Examples for Black-Box Attacks on Deep Convolutional Networks
Kenneth T. Co, Luis Muñoz-González, Sixte de Maupeou +1
Deep Convolutional Networks (DCNs) have been shown to be vulnerable to adversarial examples---perturbed inputs specifically designed to produce intentional errors in the learning a…
Mitigation of Adversarial Attacks through Embedded Feature Selection
Ziyi Bao, Luis Muñoz-González, Emil C. Lupu
Machine learning has become one of the main components for task automation in many application domains. Despite the advancements and impressive achievements of machine learning, it…
Approaches to Enhancing Cyber Resilience: Report of the North Atlantic Treaty Organization (NATO) Workshop IST-153
Alexander Kott, Benjamin Blakely, Diane Henshel +21
This report summarizes the discussions and findings of the 2017 North Atlantic Treaty Organization (NATO) Workshop, IST-153, on Cyber Resilience, held in Munich, Germany, on 23-25…
Automated Dynamic Analysis of Ransomware: Benefits, Limitations and use for Detection
Daniele Sgandurra, Luis Muñoz-González, Rabih Mohsen +1
Recent statistics show that in 2015 more than 140 millions new malware samples have been found. Among these, a large portion is due to ransomware, the class of malware whose specif…
Efficient Attack Graph Analysis through Approximate Inference
Luis Muñoz-González, Daniele Sgandurra, Andrea Paudice +1
Attack graphs provide compact representations of the attack paths that an attacker can follow to compromise network resources by analysing network vulnerabilities and topology. The…
Exact Inference Techniques for the Analysis of Bayesian Attack Graphs
Luis Muñoz-González, Daniele Sgandurra, Martín Barrère +1
Attack graphs are a powerful tool for security risk assessment by analysing network vulnerabilities and the paths attackers can use to compromise network resources. The uncertainty…