García Clemente, Félix Jesús

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García Clemente, Félix Jesús

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García Clemente, Félix Jesús

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Universidad de Murcia. Departamento de Ingeniería y Tecnología de Computadores

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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.
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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
SafeMan: a unified framework to manage cybersecurity and safety in manufacturing industry
(Wiley, 2020-08-06) Perales Gómez, Ángel Luis; Fernández Maimó, Lorenzo; García Clemente, Félix Jesús; Gil Pérez, Manuel; Martínez Pérez, Gregorio; Huertas Celdrán, Alberto; Ingeniería y Tecnología de Computadores
Industrial control systems (ICS) are considered cyber-physical systems that join both cyber and physical worlds. Due to their tight interaction, where humans and robots co-work and co-inhabit in the same workspaces and production lines, cyber-attacks targeting ICS can alter production processes and even bypass safety procedures. As an example, these cyber-attacks could interrupt physical industrial processes and cause potential injuries to workers. In this article, we present SafeMan, a unified management framework based on the Edge Computing paradigm that provides high-performance applications for the detection and mitigation of both cyber-attacks and safety threats in industrial scenarios. Three use cases show specific threats in manufacturing as well as the SafeMan actions carried out to detect and mitigate them. In order to validate our proposal, a pool of experiments was performed with Electra, an industrial dataset with normal network traffic and different cyber-attacks by using a given number of Modbus TCP and S7Comm devices. The experiments measured the runtime performance of anomaly detection techniques based on machine learning and deep learning to detect cyber-attacks in control networks. The experimental results show that Neural Networks report the best performance, being able to examine 217 feature vectors per second over Electra, and therefore demonstrating that it can be used as detection model for SafeMan in real scenarios.
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Dynamic reconfiguration in 5G mobile networks to proactively detect and mitigate botnets
(Institute of Electrical and Electronics Engineers, Computer Society, 2017-09-18) Gil Pérez, Manuel; Ippoliti, Fabrizio; Giardina, Pietro G.; Bernini, Giacomo; Marco Alaez, Ricardo; Chirivella-Perez, Enrique; García Clemente, Félix Jesús; Martínez Pérez, Gregorio; Kraja, Elian; Carrozzo, Gino; Alcaraz Calero, Jose M.; Wang, Qi; Huertas Celdrán, Alberto; Ingeniería de la Información y las Comunicaciones; Facultad de Informática
Botnets are one of the most powerful cyberthreats affecting continuity and delivery of existing network services. Detecting and mitigating attacks promoted by botnets become a greater challenge with the advent of 5G networks, as the number of connected devices with high mobility capabilities, the volume of exchange data, and the transmission rates increase significantly. Here, a 5G-oriented solution is proposed for proactively detecting and mitigating botnets in a highly dynamic 5G network. 5G subscribers’ mobility requires dynamic network reconfiguration, which is handled by combining software-defined network and network function virtualization techniques.
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SeCoMan: a semantic-aware policy framework for developing privacy-preserving and context-aware smart applications
(Institute of Electrical and Electronics Engineers, 2014-01-21) García Clemente, Félix Jesús; Gil Pérez, Manuel; Martínez Pérez, Gregorio; Huertas Celdrán, Alberto; Ingeniería de la Información y las Comunicaciones; Facultad de Informática
This paper is intended to provide a solution for developing context-aware smart applications preserving the users' privacy in the Internet of Things (IoT). In this sense, we present a framework called Semantic Web-based Context Management (SeCoMan) aimed at offering a set of predefined queries to provide applications with information about indoor location of users and objects, as well as context-aware services. SeCoMan uses a semantic-oriented IoT vision where semantic technologies play a key role. In fact, SeCoMan uses Semantic Web for modeling description of things, reasoning over data to infer new knowledge, and defining context-aware policies. SeCoMan also defines a layered architecture, including functions related to the management of the users' privacy in a manner that accommodate IoT requirements, in addition to not affecting system performance nor introducing excessive overheads. A thorough discussion on other related works, together with some experiments to measure the throughput and scalability, confirm that SeCoMan is a solution that improves the most relevant proposals existing so far.
Open Access
Design of a recommender system based on users’ behavior and collaborative location and tracking
(Elsevier, 2015-12-10) Gil Pérez, Manuel; García Clemente, Félix Jesús; Martínez Pérez, Gregorio; Huertas Celdrán, Alberto; Ingeniería de la Información y las Comunicaciones; Facultades de la UMU::Facultad de Informática
During the last years, mobile devices allow incorporating users’ location and movements into recommendations to potentially suggest most valuable information. In this context, this paper presents a hybrid recommender algorithm that combines users’ location and preferences and the content of the items located close to such users. This algorithm also includes a way of providing implicit ratings considering the users’ movements after receiving recommendations, aimed at measuring the users’ interest for the recommended items. Conducted experiments measure the effectiveness and the efficiency of our recommender algorithm, as well as the impact of implicit ratings.