RT journal article
T1 An automatic complex event processing rules generation system for the recognition of real-time IoT attack patterns
A1 Roldán Gómez, José
A1 Boubeta Puig, Juan
A1 Carrillo Mondéjar, Javier
A1 Castelo Gómez, Juan Manuel
A1 Martínez del Rincón, Jesús
A2 Ingeniería Informática
K1 CEP rules generation
K1 Cybersecurity
K1 Internet of Things
K1 Machine learning
AB The Internet of Things (IoT) has grown rapidly to become the core of many areas of application, leading to the integration of sensors, with IoT devices. However, the number of attacks against these types of devices has grown as fast as the paradigm itself. Certain inherent characteristics of the paradigm, as well as the limited computational capabilities of the devices involved, make it difficult to deploy security measures. This is why it is necessary to design, implement and study new solutions in the field of cybersecurity. In this paper, we propose an architecture that is capable of generating Complex Event Processing (CEP) rules automatically by integrating them with machine learning technologies. While the former is used to automatically detect attack patterns in real time, the latter, through the use of the Principal Component Analysis (PCA) algorithm, allows the characterization of events and the recognition of anomalies. This combination makes it possible to achieve efficient CEP rules at the computational level, with the results showing that the CEP rules obtained using our approach substantially improve upon the performance of the standard CEP rules, which are rules that are not generated by our proposal but can be defined independently by an expert in the field. Our proposal has achieved an F1-score of 0.98 on average, a 76 percent improvement in throughput over standard CEP rules, and a reduction in the network overhead of 86 percent over standard simple events, which are the simple events that are generated when our proposal is not used.
PB Elsevier
SN 0952-1976
YR 2023
FD 2023-04-28
LK http://hdl.handle.net/10498/29524
UL http://hdl.handle.net/10498/29524
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 21-sep-2026