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This work has been supported by the Spanish Ministry of Science and Innovation and the European Regional Development Funds under projects RTI2018-098156-B-C52 and RTI2018-093608-B-C33, by the JCCM under project SB-PLY/17/180501/00035, by the Spanish Ministry of Science, Innovation and Universities under grants FPU 17/02007 and FPU 17/03105, and by the University of Castilla-La Mancha under grant 2018PREDUCLM-7476.
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Castelo Gomez, Juan ManuelAutor o CoautorAttack Pattern Recognition in the Internet of Things using Complex Event Processing and Machine Learning
Publicado en:Conference Proceedings - Ieee International Conference On Systems, Man And Cybernetics. 1919-1926 - 2021-01-01 (), DOI: 10.1109/SMC52423.2021.9658711
Autores: Roldan-Gomez, Jose; Boubeta-Puig, Juan; Castelo Gomez, Juan Manuel; Carrillo-Mondejar, Javier; Martinez Martinez, Jose Luis
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Resumen
The Internet of Things (IoT) paradigm demands adapting traditional cybersecurity solutions to address the inherent limitations of IoT environments, in particular their low computational power and limited amount of memory and bandwidth. The Complex Event Processing (CEP) technology has proven to be useful in this context by deploying a CEP engine for detecting real-time attacks in an IoT network. However, CEP is only capable of detecting attacks that have been previously modeled as event patterns. This requires a domain expert who knows the conditions that must be satisfied so that certain attacks can be detected, thus identifying unmodeled ones is not possible. This paper aims to address this problem by proposing a machine learning algorithm that allows for the automatic creation of CEP patterns based on categorized data if the goal is to classify attacks, or even uncategorized data if the objective is to detect anomalies. An evaluation of the effectiveness of the automatically generated patterns for recognizing different attacks in IoT environments is also conducted in this paper.
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Impacto bibliométrico. Análisis de la aportación y canal de difusión
Desde una perspectiva relativa, y atendiendo al indicador del impacto normalizado calculado a partir del Field Citation Ratio (FCR) de la fuente Dimensions, arroja un valor de: 1.66, lo que indica que, de manera comparada con trabajos en la misma disciplina y en el mismo año de publicación, lo ubica como trabajo citado por encima de la media. (fuente consultada: Dimensions Jul 2025)
De manera concreta y atendiendo a las diferentes agencias de indexación, el trabajo ha acumulado, hasta la fecha 2025-07-05, el siguiente número de citas:
- WoS: 6
- Scopus: 7
- Open Alex: 6