Javier Alejandro Maroto Morales

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Orthogonal frequency-division multiplexing (OFDM) is widely used to mitigate inter-symbol interference (ISI) from multipath fading. However, the open nature of wireless OFDM systems makes them vulnerable to jamming attacks. In this context, pilot jamming i ...

With the increasing amount of available data and advances in computing capabilities, deep neural networks (DNNs) have been successfully employed to solve challenging tasks in various areas, including healthcare, climate, and finance. Nevertheless, state-of ...

In communication systems, there are many tasks, like modulation classification, for which Deep Neural Networks (DNNs) have obtained promising performance. However, these models have been shown to be susceptible to adversarial perturbations, namely impercep ...

Anomaly Detection systems based on Machine and Deep learning are the most promising solutions to detect cyberattacks in the industry. However, these techniques are vulnerable to adversarial attacks that downgrade prediction performance. Several techniques ...

Current state of the art algorithms for recommender systems are mainly based on collaborative filtering, which exploits user ratings to discover latent factors in the data. These algorithms unfortunately do not make effective use of other features, which c ...

Javier Alejandro Maroto Morales

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Adversarial Training for Jamming-Robust Channel Estimation in OFDM Systems

Adversarial training with informed data selection

SafeAMC: Adversarial training for robust modulation classification models

A Methodology for Evaluating the Robustness of Anomaly Detectors to Adversarial Attacks in Industrial Scenarios

Modurec: Recommender Systems with Feature and Time Modulation