9 citations · 19 across the 5 of their papers we have counts for
5 papers
Topological safeguard for evasion attack interpreting the neural networks' behavior
Xabier Echeberria-Barrio, Amaia Gil-Lerchundi, Iñigo Mendialdua +1
In the last years, Deep Learning technology has been proposed in different fields, bringing many advances in each of them, but identifying new threats in these solutions regarding…
NeuralSentinel: Safeguarding Neural Network Reliability and Trustworthiness
Xabier Echeberria-Barrio, Mikel Gorricho, Selene Valencia +1
The usage of Artificial Intelligence (AI) systems has increased exponentially, thanks to their ability to reduce the amount of data to be analyzed, the user efforts and preserving…
Understanding Deep Learning defenses Against Adversarial Examples Through Visualizations for Dynamic Risk Assessment
Xabier Echeberria-Barrio, Amaia Gil-Lerchundi, Jon Egana-Zubia +1
In recent years, Deep Neural Network models have been developed in different fields, where they have brought many advances. However, they have also started to be used in tasks wher…
NBcoded: network attack classifiers based on Encoder and Naive Bayes model for resource limited devices
Lander Segurola-Gil, Francesco Zola, Xabier Echeberria-Barrio +1
In the recent years, cybersecurity has gained high relevance, converting the detection of attacks or intrusions into a key task. In fact, a small breach in a system, application, o…
Deep Learning Defenses Against Adversarial Examples for Dynamic Risk Assessment
Xabier Echeberria-Barrio, Amaia Gil-Lerchundi, Ines Goicoechea-Telleria +1
Deep Neural Networks were first developed decades ago, but it was not until recently that they started being extensively used, due to their computing power requirements. Since then…