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Gil Pérez, Manuel

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Gil Pérez, Manuel
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Universidad de Murcia. Departamento de Ingeniería de la Informacióny 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.
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    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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