RT journal article
T1 An automatic unsupervised complex event processing rules generation architecture for real-time IoT attacks detection
A1 Roldán Gómez, José
A1 Martínez del Rincón, Jesús
A1 Boubeta Puig, Juan
A1 Martínez, José Luis
A2 Ingeniería Informática
K1 Attack detection
K1 Complex event processing
K1 Cybersecurity
K1 Internet of things
K1 Machine learning
AB In recent years, the Internet of Things (IoT) has grown rapidly, as has the number of attacks against it. Certain limitations of the paradigm, such as reduced processing capacity and limited main and secondary memory, make it necessary to develop new methods for detecting attacks in real time as it is difficulty to adapt as has the techniques used in other paradigms. In this paper, we propose an architecture capable of generating complex event processing (CEP) rules for real-time attack detection in an automatic and completely unsupervised manner. To this end, CEP technology, which makes it possible to analyze and correlate a large amount of data in real time and can be deployed in IoT environments, is integrated with principal component analysis (PCA), Gaussian mixture models (GMM) and the Mahalanobis distance. This architecture has been tested in two different experiments that simulate real attack scenarios in an IoT network. The results show that the rules generated achieved an F1 score of.9890 in detecting six different IoT attacks in real time.
PB Springer
SN 1022-0038
YR 2023
FD 2023-01-16
LK http://hdl.handle.net/10498/29387
UL http://hdl.handle.net/10498/29387
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 21-sep-2026