RT journal article
T1 Integrating complex event processing and machine learning: An intelligent architecture for detecting IoT security attacks
A1 Roldán, José
A1 Boubeta Puig, Juan
A1 Martínez, José Luis
A1 Ortiz Bellot, Guadalupe
A2 Ingeniería Informática
K1 Complex event processing
K1 Machine learning
K1 Software architecture
K1 Intelligent decision making
K1 Internet of Things
K1 Security attack
AB The Internet of Things (IoT) is growing globally at a fast pace: people now find themselves surrounded by a variety of IoT devices such as smartphones and wearables in their everyday lives. Additionally, smart environments, such as smart healthcare systems, smart industries and smart cities, benefit from sensors and actuators interconnected through the IoT. However, the increase in IoT devices has brought with it the challenge of promptly detecting and combating the cybersecurity attacks and threats that target them, including malware, privacy breaches and denial of service attacks, among others. To tackle this challenge, this paper proposes an intelligent architecture that integrates Complex Event Processing (CEP) technology and the Machine Learning (ML) paradigm in order to detect different types of IoT security attacks in real time. In particular, such an architecture is capable of easily managing event patterns whose conditions depend on values obtained by ML algorithms. Additionally, a model-driven graphical tool for security attack pattern definition and automatic code generation is provided, hiding all the complexity derived from implementation details from domain experts. The proposed architecture has been applied in the case of a healthcare IoT network to validate its ability to detect attacks made by malicious devices. The results obtained demonstrate that this architecture satisfactorily fulfils its objectives.
PB Elsevier Ltd.
SN 0957-4174
YR 2020
FD 2020-07-01
LK http://hdl.handle.net/10498/29841
UL http://hdl.handle.net/10498/29841
LA eng
NO This work was supported by the Spanish Ministry of Science, Innovation and Universities and the European Union FEDER Funds [grant numbers FPU 17/02007, RTI2018-093608-B-C33, RTI2018- 098156-B-C52 and RED2018-102654-T]. This work was also sup- ported by the JCCM [grant number SB-PLY/17/180501/ 0 0 0353 ]
DS Repositorio Institucional de la Universidad de Cádiz
RD 12-nov-2026