Exploring the Affordances of Multimodal Data to Improve Cybersecurity Training with Cyber Range Environments

Albaladejo-González, Mariano; Strukova, Sofia; Ruipérez‐Valiente, José A.; Mármol, Félix Gómez

doi:10.18239/jornadas_2021.34.52

Cited by 3 publications

(2 citation statements)

References 12 publications

(13 reference statements)

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“…21 Additionally, there is also an increasing interest in how games can be effectively applied in learning and training contexts, as well as in other areas such as healthcare, 22 rehabilitation, 23 and military training. 24 For instance, Albaladejo et al 25 presented a multimodal system that could be used to improve cyberdefense capabilities by utilizing gamified platforms for training and testing individuals and organizations in cybersecurity practices and techniques. However, SGs must be able to show that the necessary learning has occurred.…”

Section: Serious Games and Game-based Assessmentmentioning

confidence: 99%

A framework to support interoperable Game‐based Assessments as a Service (GBAaaS): Design, development, and use cases

Gomez,

Ruipérez‐Valiente,

García Clemente

2023

Softw Pract Exp

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During the last few years, there has been increasing attention paid to serious games (SGs), which are games used for non‐entertainment purposes. SGs offer the potential for more valid and reliable assessments compared to traditional methods such as paper‐and‐pencil tests. However, the incorporation of assessment features into SGs is still in its early stages, requiring specific design efforts for each game and adding significant time to the design of Game‐based Assessments (GBAs). In this research, we present a completely novel framework that aims to perform interoperable GBAs by: (a) integrating a common GBA ontology model to process RDF data; (b) developing in‐game metrics to infer useful information and assess learners; (c) integrating a service API to provide an easy way to interact with the framework. We then validate our approach through performance evaluation and two use cases, demonstrating its effectiveness in real‐world scenarios with large‐scale datasets. Our results show that the developed framework achieves excellent performance, replicating metrics from previous literature. We anticipate that our work will help alleviate current limitations in the field and facilitate the deployment of GBAs as a Service.

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Section: Serious Games and Game-based Assessmentmentioning

confidence: 99%

A framework to support interoperable Game‐based Assessments as a Service (GBAaaS): Design, development, and use cases

Gomez,

Ruipérez‐Valiente,

García Clemente

2023

Softw Pract Exp

Self Cite

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“…What would be the requirements for AI decisions to be reliable? [13]. How will it be possible to avoid the so-called black box phenomenon in the choice of patients who will receive health resources?…”

Section: Introductionmentioning

confidence: 99%

The Language of Nature and Artificial Intelligence in Patient Care

Enríquez

Alonso-Stuyck

Martínez-Villaseñor

2023

IJERPH

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Given the development of artificial intelligence (AI) and the conditions of vulnerability of large sectors of the population, the question emerges: what are the ethical limits of technologies in patient care? This paper examines this question in the light of the “language of nature” and of Aristotelian causal analysis, in particular the concept of means and ends. Thus, it is possible to point out the root of the distinction between the identity of the person and the entity of any technology. Nature indicates that the person is always an end in itself. Technology, on the contrary, should only be a means to serve the person. The diversity of their respective natures also explains why their respective agencies enjoy diverse scopes. Technological operations (artificial agency, artificial intelligence) find their meaning in the results obtained through them (poiesis). Moreover, the person is capable of actions whose purpose is precisely the action itself (praxis), in which personal agency and, ultimately, the person themselves, is irreplaceable. Forgetting the distinction between what, by nature, is an end and what can only be a means is equivalent to losing sight of the instrumental nature of AI and, therefore, its specific meaning: the greatest good of the patient. It is concluded that the language of nature serves as a filter that supports the effective subordination of the use of AI to its specific purpose, the human good. The greatest contribution of this work is to draw attention to the nature of the person and technology, and about their respective agencies. In other words: listening to the language of nature, and attending to the diverse nature of the person and technology, personal agency, and artificial agency.

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Evaluating different configurations of machine learning models and their transfer learning capabilities for stress detection using heart rate

Albaladejo-González

Ruipérez‐Valiente

Mármol

2022

J Ambient Intell Human Comput

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In the twentyfirst-century society, several soft skills are fundamental, such as stress management, which is considered one of the key ones due to its strong relationship with health and well-being. However, this skill is hard to measure and master without external support. This paper tackles stress detection through artificial intelligence (AI) models and heart rate, analyzing in WESAD and SWELL-KW datasets five supervised models and five unsupervised anomaly detection models that had not been tested before for stress detection. Also, we analyzed the transfer learning capabilities of the AI models since it is an open issue in the stress detection field. The models with the highest performance on test data were the anomaly detection Local Outlier Factor (LOF) with F1-scores of 88.89% in WESAD and 77.17% in SWELL-KW, and the supervised Multi-layer Perceptron (MLP) with F1-scores of 99.03% in WESAD and 82.75% in SWELL-KW. However, when evaluating the transfer learning capabilities of these AI models, MLP performed much worse on the other dataset, decreasing the F1-score to 28.41% in SWELL-KW and 57.28% in WESAD. In contrast, LOF reported better transfer learning performance achieving F1-scores of 70.66% in SWELL-KW and 85.00% in WESAD. Finally, we found that training AI models with both datasets (i.e., with data from different contexts) improved the average performance of the models and their generalization; with this setup, LOF achieved F1-scores of 87.92% and 85.51% in WESAD, and 78.03% and 82.16% in SWELL-KW; whereas MLP obtained 78.36% and 81.33% in WESAD, and 79.37% and 80.68% in SWELL-KW. Therefore, we suggest as a promising direction the use of anomaly detection models or multi-contextual training to improve the transfer learning capabilities in this field, which is a novelty in the literature. We believe that these AI models combined with the use of non-invasive wearables can enable a new generation of stress management mobile applications.

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Exploring the Affordances of Multimodal Data to Improve Cybersecurity Training with Cyber Range Environments

Abstract: Colección JORNADAS Y CONGRESOS n.º 34 Esta editorial es miembro de la UNE, lo que garantiza la difusión y comercialización de sus publicaciones a nivel nacional e internacional.

Cited by 3 publications

References 12 publications

A framework to support interoperable Game‐based Assessments as a Service (GBAaaS): Design, development, and use cases

A framework to support interoperable Game‐based Assessments as a Service (GBAaaS): Design, development, and use cases

The Language of Nature and Artificial Intelligence in Patient Care

Evaluating different configurations of machine learning models and their transfer learning capabilities for stress detection using heart rate

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