Persona: Robles Gómez, Antonio
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Robles Gómez
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Publicación Forensic Analysis Laboratory for Sport Devices: A Practical Case of Use(MDPI, 2023) Donaire Calleja, Pablo; Robles Gómez, Antonio; Tobarra Abad, María de los Llanos; Pastor Vargas, RafaelAt present, the mobile device sector is experiencing significant growth. In particular, wear- 1 able devices have become a common element in society. This fact implies that users unconsciously 2 accept the constant dynamic collection of private data about their habits and behaviours. Therefore, 3 this work focuses on highlighting and analyzing some of the main issues that forensic analysts face 4 in this sector, such as the lack of standard procedures for analysis and the common use of private 5 protocols for data communication. Thus, it is almost impossible for a digital forensic specialist to 6 fully specialize in the context of wearables, such as smartwatches for sports activities. With the aim 7 of highlighting these problems, a complete forensic analysis laboratory for such sports devices is 8 described in this paper. We selected a smartwatch belonging to the Garmin Forerunner Series, due to 9 its great popularity. Through an analysis, its strengths and weaknesses in terms of data protection 10 are described. We also analyze how companies are increasingly taking personal data privacy into 11 consideration, in order to minimize unwanted information leaks. Finally, a set of initial security 12 recommendations for the use of these kinds of devices are provided to the reader.Publicación A Cloud Game-based Educative Platform Architecture: the CyberScratch Project(MDPI, 2021) Utrilla, Alejandro; Tobarra Abad, María de los Llanos; Robles Gómez, Antonio; Pastor Vargas, Rafael; Hernández Berlinches, RobertoThe employment of modern technologies is widespread in our society, so the inclusion of practical activities for education has become essential and useful at the same time. These activities are more noticeable in Engineering, in areas such as cybersecurity, data science, artificial intelligence, etc. Additionally, these activities acquire even more relevance with a distance education methodology, as our case is. The inclusion of these practical activities has clear advantages , such as (1) promoting critical thinking and (2) improving students’ abilities and skills for their professional careers. There are several options, such as the use of remote and virtual laboratories, virtual reality and gamebased platforms, among others. This work addresses the development of a new cloud game-based educational platform, which defines a modular and flexible architecture (using light containers). This architecture provides interactive and monitoring services and data storage in a transparent way. The platform uses gamification to integrate the game as part of the instructional process. The CyberScratch project is a particular implementation of this architecture focused on cybersecurity game-based activities. The data privacy management is a critical issue for these kinds of platforms, so the architecture is designed with this feature integrated in the platform components. To achieve this goal, we first focus on all the privacy aspects for the data generated by our cloud game-based platform, by considering the European legal context for data privacy following GDPR and ISO/IEC TR 20748-1:2016 recommendations for Learning Analytics (LA). Our second objective is to provide implementation guidelines for efficient data privacy management for our cloud game-based educative platform. All these contributions are not found in current related works. The CyberScratch project, which was approved by UNED for the year 2020, considers using the xAPI standard for data handling and services for the game editor, game engine and game monitor modules of CyberScratch. Therefore, apart from considering GDPR privacy and LA recommendations, our cloud game-based architecture covers all phases from game creation to the final users’ interactions with the game.Publicación Internet of Things Remote Laboratory for MQTT remote experimentation(Springer Link, 2023) Anhelo, Jesús; Robles Gómez, Antonio; Martín Gutiérrez, SergioRemote laboratories have matured substantially and have seen widespread adoption across universities globally. This paper delineates the design and implementation of a remote laboratory for Industry 4.0, specifically for Internet of Things. It employs Raspberry Pi and ESP8266 microcontrollers, to bolster online Internet of Things (IoT) learning and experimentation platforms. Such platforms hold significant value in delivering high-quality online education programs centered on IoT. Students have access to a web interface where they can write Arduino code to program the behavior of each one of the nodes of an Internet of Things scenario. This setup allows them to remotely program three NodeMCU boards in a manner akin to the usage of the Arduino IDE connected to an Arduino board locally. The system offers the ability to compile and upload code, complete with error notifications. Additionally, it furnishes several functionalities such as the ability to load new local code, save the authored code to one's personal computer, load predefined examples, access a serial monitor, and avail the Node Red platform. This amalgamation of features promises to offer a comprehensive and interactive remote learning experience for students engaging with IoT technologies.Publicación Analyzing the Users’ Acceptance of an IoT Cloud Platform using the UTAUT/TAM Model(Institute of Electrical and Electronics Engineers, 2021) Haut, Juan M.; Robles Gómez, Antonio; Tobarra Abad, María de los Llanos; Pastor Vargas, Rafael; Hernández Berlinches, RobertoAntonio Robles-Gómez, Llanos Tobarra, Rafael Pastor-Vargas, Roberto Hernández, Juan M. Haut; Título:; Publicación: . ISSN (https://doi.org/10.1109/ACCESS.2021.3125497);Publicación Dataset Generation and Study of Deepfake Techniques(Springer, 2023) Falcón López, Sergio Adrián; Robles Gómez, Antonio; Tobarra Abad, María de los Llanos; Pastor Vargas, RafaelThe consumption of multimedia content on the Internet has nowadays been expanded exponentially. These trends have contributed to fake news can become a very high influence in the current society. The latest techniques to influence the spread of digital false information are based on methods of generating images and videos, known as Deepfakes. This way, our research work analyzes the most widely used Deepfake content generation methods, as well as explore different conventional and advanced tools for Deepfake detection. A specific dataset has also been built that includes both fake and real multimedia contents. This dataset will allow us to verify whether the used image and video forgery detection techniques can detect manipulated multimedia content.Publicación Emulating and Evaluating Virtual Remote Laboratories for Cybersecurity(MDPI, 2020) Cano, Jesús; Robles Gómez, Antonio; Tobarra Abad, María de los Llanos; Pastor Vargas, Rafael; Hernández Berlinches, RobertoOur society is nowadays evolving towards a digital era, due to the extensive use of computer technologies and their interconnection mechanisms, i.e., social networks, Internet resources, IoT services, etc. This way, new threats and vulnerabilities appear. Therefore, there is an urgent necessity of training students in the topic of cybersecurity, in which practical skills have to be acquired. In distance education, the inclusion of on-line resources for hands-on activities in its curricula is a key step in meeting that need. This work presents several contributions. First, the fundamentals of a virtual remote laboratory hosted in the cloud are detailed. This laboratory is a step forward since the laboratory combines both virtualization and cloud paradigms to dynamically create emulated environments. Second, this laboratory has also been integrated into the practical curricula of a cybersecurity subject, as an additional on-line resource. Third, the students’ traceability, in terms of their interactions with the laboratory, is also analyzed. Psychological TAM/UTAUT factors (perceived usefulness, estimated effort, social influence, attitude, ease of access) that may affect the intention of using the laboratory are analyzed. Fourth, the degree of satisfaction is analyzed with a great impact, since the mean values of these factors are most of them higher than 4 points out of 5. In addition to this, the students’ acceptance of the presented technology is exhaustively studied. Two structural equation models have been hypothesized and validated. Finally, the acceptance of the technology can be concluded as very good in order to be used in @? other Engineering contexts. In this sense, the calculated statistical values for the improved proposed model are within the expected ranges of reliability (X2 = 0.6, X2/DF = 0.3, GFI = 0.985, CIF = 0.985, RMSEA = 0) by considering the literaturePublicación Easy Development of Industry 4.0 Remote Labs(MDPI, 2024) Rejón Gómez, Carlos; Martín Gutiérrez, Sergio; Robles Gómez, AntonioAcquiring hands-on skills is nowadays key for Engineers today in the context of Industry 1 4.0. However, it is not always possible to do this in person. Therefore, it is essential to be able to do 2 this from a remote location. To support the development of remote labs for experimentation, this work 3 proposes the development of an open Industry 4.0 remote platform, which can be easily configured 4 and scaled to develop new remote labs for IoT (Internet of Things), cybersecurity, perception systems, 5 robotics, AI (Artificial Intelligence), etc. Over time, these capabilities will enable the development of 6 sustainable Industry 4.0 remote labs. These labs will coexist on the same Industry 4.0 platform, as 7 our proposed Industry 4.0 remote platform is capable of connecting multiple heterogeneous types 8 of devices for remote programming. In this way, it is possible to easily design open remote labs for 9 the digital transition to Industry 4.0 in a standardized way, which is the main research goal of our 10 In4Labs project. Several users are already conducting a series of IoT experiments within our remote 11 Industry 4.0 platform, providing useful recommendations to be included in future versions of the 12 platform.Publicación Easy Development of Industry 4.0 Remote Labs(MDPI, 2024) Rejón Gómez, Carlos; Martín Gutiérrez, Sergio; Robles Gómez, AntonioAcquiring hands-on skills is nowadays key for Engineers today in the context of Industry 1 4.0. However, it is not always possible to do this in person. Therefore, it is essential to be able to do 2 this from a remote location. To support the development of remote labs for experimentation, this work 3 proposes the development of an open Industry 4.0 remote platform, which can be easily configured 4 and scaled to develop new remote labs for IoT (Internet of Things), cybersecurity, perception systems, 5 robotics, AI (Artificial Intelligence), etc. Over time, these capabilities will enable the development of 6 sustainable Industry 4.0 remote labs. These labs will coexist on the same Industry 4.0 platform, as 7 our proposed Industry 4.0 remote platform is capable of connecting multiple heterogeneous types 8 of devices for remote programming. In this way, it is possible to easily design open remote labs for 9 the digital transition to Industry 4.0 in a standardized way, which is the main research goal of our 10 In4Labs project. Several users are already conducting a series of IoT experiments within our remote 11 Industry 4.0 platform, providing useful recommendations to be included in future versions of the 12 platform.Publicación Exploring IoT Vulnerabilities in a Comprehensive Remote Cybersecurity Laboratory(MDPI, 2023) Delgado, Ismael; San Cristóbal Ruiz, Elio; Martín Gutiérrez, Sergio; Robles Gómez, AntonioWith the rapid proliferation of Internet of things (IoT) devices across various sectors, ensuring robust cybersecurity practices has become paramount. The complexity and diversity of IoT ecosystems pose unique security challenges that traditional educational approaches often fail to address comprehensively. Current curricula may provide theoretical knowledge but typically lack the practical components necessary for students to engage with real-world cybersecurity scenarios. This gap hinders the development of proficient cybersecurity professionals capable of securing complex IoT infrastructures. To bridge this educational divide, a remote online laboratory was developed, allowing students to gain hands-on experience in identifying and mitigating cybersecurity threats in an IoT context. This virtual environment simulates real IoT ecosystems, enabling students to interact with actual devices and protocols while practicing various security techniques. The laboratory is designed to be accessible, scalable, and versatile, offering a range of modules from basic protocol analysis to advanced threat management. The implementation of this remote laboratory demonstrated significant benefits, equipping students with the necessary skills to confront and resolve IoT security issues effectively. Our results show an improvement in practical cybersecurity abilities among students, highlighting the laboratory’s efficacy in enhancing IoT security education.Publicación SiCoDeF² Net: Siamese Convolution Deconvolution Feature Fusion Network for One-Shot Classification(IEEE, 2021) Kumar Roy, Swalpa; Kar, Purbayan; Paoletti, Mercedes E.; Haut, Juan M.; Pastor Vargas, Rafael; Robles Gómez, AntonioNowadays, deep convolutional neural networks (CNNs) for face recognition exhibit a performance comparable to human ability in the presence of the appropriate amount of labelled training data. However, training CNNs remains as an arduous task due to the lack of training samples. To overcome this drawback, applications demand one-shot learning to improve the obtained performances over traditional machine learning approaches by learning representative information about data categories from few training samples. In this context, Siamese convolutional network ( SiConvNet ) provides an interesting deep architecture to tackle the data limitation. In this regard, applying the convolution operation on real world images by using the trainable correlative Gaussian kernel adds correlations to the output images, which hinder the recognition process due to the blurring effects introduced by the convolution kernel application. As a result the pixel-wise and channel-wise correlations or redundancies could appear in both single and multiple feature maps obtained by a hidden layer. In this sense, convolution-based models fail to generalize the feature representation because of both the strong correlations presence in neighboring pixels and the channel-wise high redundancies between different channels of the feature maps, which hamper the effective training. Deconvolution operation helps to overcome the shortcomings that limit the conventional SiConvNet performance, learning successfully correlation-free features representation. In this paper, a simple but efficient Siamese convolution deconvolution feature fusion network ( SiCoDeF 2 Net ) is proposed to learn the invariant and discriminative complementary features generated from both the (i) sub-convolution (SCoNet) and (ii) sub deconvolutional (SDeNet) networks using a concatenation operation which significantly improves the one-shot unconstrained facial recognition task. Extensive experiments performed on several widely used benchmarks, provide promising results, where the proposed SiCoDeF 2 Net model significantly outperforms the current state-of-art in terms of classification accuracy, F1, precision and recall. The code will be available on: https://github.com/purbayankar/SiCoDeF2Net .