Integrating Complex Event Processing and Machine Learning: an Intelligent Architecture for Detecting IoT Security Attacks

• Autores: José Roldán Gómez, Juan Boubeta Puig , Jose Luis Martinez Martinez , Guadalupe Ortiz
• Localización: Actas de las XXV Jornadas de Ingeniería del Software y Bases de Datos (JISBD 2021): [Málaga, 22 al 24 de septiembre de 2021] / coord. por Rafael Capilla Sevilla , Maider Azanza Sese , Miguel Rodríguez Luaces , M. M. Roldán García , Dolores Burgueño Caballero, José Raúl Romero Salguero , José Antonio Parejo Maestre , José Francisco Chicano García , Marcela Genero , Óscar Díaz García , José González Enríquez , María Carmen Penades Gramage ; Silvia Mara Abrahao Gonzales (col.) , 2021
• Idioma: inglés
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• Resumen

  • The Internet of Things (IoT) is growing globally at a fast pace. However, the increase in IoT devices has brought with it the challenge of promptly detecting and combating the cybersecurity threats that target them. To deal with this problem, we propose 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.