Huertas Celdrán, Alberto

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Huertas Celdrán, Alberto

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Huertas Celdrán, Alberto

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Ingeniería de la Información y las Comunicaciones

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Dynamic management of a deep learning-based anomaly detection system for 5G networks
(Springer, 2018-05-05) Fernández Maimó, Lorenzo; Gil Pérez, Manuel; García Clemente, Félix Jesús; Martínez Pérez, Gregorio; Huertas Celdrán, Alberto; Ingeniería y Tecnología de Computadores
Fog and mobile edge computing (MEC) will play a key role in the upcoming fifth generation (5G) mobile networks to support decentralized applications, data analytics and management into the network itself by using a highly distributed compute model. Furthermore, increasing attention is paid to providing user-centric cybersecurity solutions, which particularly require collecting, processing and analyzing significantly large amount of data traffic and huge number of network connections in 5G networks. In this regard, this paper proposes a MEC-oriented solution in 5G mobile networks to detect network anomalies in real-time and in autonomic way. Our proposal uses deep learning techniques to analyze network flows and to detect network anomalies. Moreover, it uses policies in order to provide an efficient and dynamic management system of the computing resources used in the anomaly detection process. The paper presents relevant aspects of the deployment of the proposal and experimental results to show its performance.
Open Access
Fedstellar: a platform for decentralized federated learning
(Elsevier, 2024-05-14) Martínez Beltrán, Enrique Tomás; Perales Gómez, Ángel Luis; Feng, Chao; Sánchez Sánchez, Pedro Miguel; López Bernal, Sergio; Gérôme, Bovet; Gil Pérez, Manuel; Martínez Pérez, Gregorio; Huertas Celdrán, Alberto; Ingeniería y Tecnología de Computadores
In 2016, Google proposed Federated Learning (FL) as a novel paradigm to train Machine Learning (ML) models across the participants of a federation while preserving data privacy. Since its birth, Centralized FL (CFL) has been the most used approach, where a central entity aggregates participants’ models to create a global one. However, CFL presents limitations such as communication bottlenecks, single point of failure, and reliance on a central server. Decentralized Federated Learning (DFL) addresses these issues by enabling decentralized model aggregation and minimizing dependency on a central entity. Despite these advances, current platforms training DFL models struggle with key issues such as managing heterogeneous federation network topologies, adapting the FL process to virtualized or physical deployments, and using a limited number of metrics to evaluate different federation scenarios for efficient implementation. To overcome these challenges, this paper presents Fedstellar, a novel platform designed to train FL models in a decentralized, semi-decentralized, and centralized fashion across diverse federations of physical or virtualized devices. Fedstellar allows users to create federations by customizing parameters like the number and type of devices training FL models, the network topology connecting them, the machine and deep learning algorithms, or the datasets of each participant, among others. Additionally, it offers real-time monitoring of model and network performance. The Fedstellar implementation encompasses a web application with an interactive graphical interface, a controller for deploying federations of nodes using physical or virtual devices, and a core deployed on each device, which provides the logic needed to train, aggregate, and communicate in the network. The effectiveness of the platform has been demonstrated in two scenarios: a physical deployment involving single-board devices such as Raspberry Pis for detecting cyberattacks and a virtualized deployment comparing various FL approaches in a controlled environment using MNIST and CIFAR-10 datasets. In both scenarios, Fedstellar demonstrated consistent performance and adaptability, achieving of 91%, 98%, and 91.2% using DFL for detecting cyberattacks and classifying MNIST and CIFAR-10, respectively, reducing training time by 32% compared to centralized approaches.
Open Access
A supervised ML Biometric Continuous Authentication System for Industry 4.0
(IEEE, 2022-04-29) Espín López, Juan Manuel; Esquembre, Francisco; Martínez Pérez, Gregorio; Marín-Blázquez, Javier G.; Huertas Celdrán, Alberto; Matemáticas
Continuous authentication (CA) is a promis- ing approach to authenticate workers and avoid security breaches in the industry, especially in Industry 4.0, where most interaction between workers and devices takes place. However, introducing CA in industries raises unsolved questions regarding machine learning (ML) models: i) its precision and performance, ii) its robustness and iii) the issue about if or when to retrain the models. To answer these questions, this work explores these issues with a proposed supervised vs non-supervised ML-based CA sys- tem that uses sensors, applications statistics, or speaker data collected by the operator’s devices. Experiments show supervised models with Equal Error Rates of 7.28% using sensors data, 9.29% with statistics, and 0.31% with voice, a significant improvement of 71.97%, 62.14%, and 97.08%, respectively, over unsupervised models. Voice is the most robust dimension when adding new workers, with less than 2% of false acceptance rate even if workforce size is doubled.
Open Access
Extensiones de Machine Learning. Trustworthy Federated Learning (FL)
(Universidad de Murcia, 2026) Huertas Celdrán, Alberto; Ingeniería de la Información y las Comunicaciones; Facultades de la UMU::Facultad de Informática
Open Access
Data fusion in neuromarketing: multimodal analysis of biosignals, lifecycle stages, current advances, datasets, trends, and challenges
(Elsevier, 2024-01-05) Quiles Pérez, Mario; Martínez Beltrán, Enrique Tomás; López Bernal, Sergio; Horna Prat, Eduardo; Montesano Del Campo, Luis; Fernández Maimó, Lorenzo; Huertas Celdrán, Alberto; Ingeniería y Tecnología de Computadores; Facultad de Informática
The primary goal of any company is to increase its profits by improving both the quality of its products and how they are advertised. In this context, neuromarketing seeks to enhance the promotion of products and generate a greater acceptance on potential buyers. Traditionally, neuromarketing studies have relied on a single biosignal to obtain feedback from presented stimuli. However, thanks to new devices and technological advances studying this area of knowledge, recent trends indicate a shift towards the fusion of diverse biosignals. An example is the usage of electroencephalography for understanding the impact of an advertisement at the neural level and visual tracking to identify the stimuli that induce such impacts. This emerging pattern determines which biosignals to employ for achieving specific neuromarketing objectives. Furthermore, the fusion of data from multiple sources demands advanced processing methodologies. Despite these complexities, there is a lack of literature that adequately collates and organizes the various data sources and the applied processing techniques for the research objectives pursued. To address these challenges, the current paper conducts a comprehensive analysis of the objectives, biosignals, and data processing techniques employed in neuromarketing research. This study provides both the technical definition and a graphical distribution of the elements under revision. Additionally, it presents a categorization based on research objectives and provides an overview of the combinatory methodologies employed. After this, the paper examines primary public datasets designed for neuromarketing research together with others whose main purpose is not neuromarketing, but can be used for this matter. Ultimately, this work provides a historical perspective on the evolution of techniques across various phases over recent years and enumerates key lessons learned.
Open Access
Extensiones de Machine Learning. Federated Learning (FL)
(Universidad de Murcia, 2026) Huertas Celdrán, Alberto; Ingeniería de la Información y las Comunicaciones; Facultades de la UMU::Facultad de Informática
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Precise: privacy-aware recommender based on context information for cloud service environments
(Institute of Electrical and Electronics Engineers, 2014-08-31) Gil Pérez, Manuel; García Clemente, Félix Jesús; Martínez Pérez, Gregorio; Huertas Celdrán, Alberto; Ingeniería de la Información y las Comunicaciones; Facultad de Informática
Context-aware systems based on location open up new possibilities to users in terms of acquiring custom services by gathering context information, especially in systems where the high mobility of users increases their usability. In this context, this article presents a privacy-preserving solution offering context-aware services based on location in MCC. We propose a middleware, called PRECISE, which provides users with custom context-aware recommendations. These recommendations are given by considering the context information, and the users' locations, privacy policies, and previously visited places. MCC plays a key role in this solution, moving the data processing and storage needs to the cloud, as well as further advantages such as elasticity and load balancing. A thorough discussion when comparing PRECISE with other related works confirms that our solution improves the most relevant proposals so far.
Open Access
S3: An AI-Enabled User Continuous Authentication for Smartphones Based on Sensors, Statistics and Speaker Information
(MDPI, 2021-05-28) Espín López, Juan Manuel; Marín-Blázquez, Javier G.; Esquembre, Francisco; Martínez Pérez, Gregorio; Huertas Celdrán, Alberto; Matemáticas
Continuous authentication systems have been proposed as a promising solution to au- thenticate users in smartphones in a non-intrusive way. However, current systems have important weaknesses related to the amount of data or time needed to build precise user profiles, together with high rates of false alerts. Voice is a powerful dimension for identifying subjects but its suitability and importance have not been deeply analyzed regarding its inclusion in continuous authentication systems. This work presents the S3 platform, an artificial intelligence-enabled continuous authen- tication system that combines data from sensors, applications statistics and voice to authenticate users in smartphones. Experiments have tested the relevance of each kind of data, explored different strategies to combine them, and determined how many days of training are needed to obtain good enough profiles. Results showed that voice is much more relevant than sensors and applications statistics when building a precise authenticating system, and the combination of individual models was the best strategy. Finally, the S3 platform reached a good performance with only five days of use available for training the users’ profiles. As an additional contribution, a dataset with 21 volun- teers interacting freely with their smartphones for more than sixty days has been created and made available to the community.
Open Access
Extensiones de Machine Learning. Security in Federated Learning (FL)
(Universidad de Murcia, 2026) Huertas Celdrán, Alberto; Ingeniería de la Información y las Comunicaciones; Facultades de la UMU::Facultad de Informática
Open Access
A methodology for evaluating the robustness of anomaly detectors to adversarial attacks in industrial scenarios
(IEEE, 2022-11-28) Perales Gómez, Ángel Luis; Fernández Maimó, Lorenzo; García Clemente, Félix J.; Maroto Morales, Alejandro; Huertas Celdrán, Alberto; Bovet, Gérôme; Huertas Celdrán, Alberto; Ingeniería y Tecnología de Computadores
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 have been proposed to measure the robustness of Anomaly Detection in the literature. However, they do not consider that, although a small perturbation in an anomalous sample belonging to an attack, i.e., Denial of Service, could cause it to be misclassified as normal while retaining its ability to damage, an excessive perturbation might also transform it into a truly normal sample, with no real impact on the industrial system. This paper presents a methodology to calculate the robustness of Anomaly Detection models in industrial scenarios. The methodology comprises four steps and uses a set of additional models called support models to determine if an adversarial sample remains anomalous. We carried out the validation using the Tennessee Eastman process, a simulated testbed of a chemical process. In such a scenario, we applied the methodology to both a Long-Short Term Memory (LSTM) neural network and 1-dimensional Convolutional Neural Network (1D-CNN) focused on detecting anomalies produced by different cyberattacks. The experiments showed that 1D-CNN is significantly more robust than LSTM for our testbed. Specifically, a perturbation of 60% (empirical robustness of 0.6) of the original sample is needed to generate adversarial samples for LSTM, whereas in 1D-CNN the perturbation required increases up to 111% (empirical robustness of 1.11).