FEMa-FS: Finite Element Machines for Feature Selection

• Autor(a) principal: Biaggi, Lucas [UNESP]
• Data de Publicação: 2022
• Outros Autores: Papa, Joao P. [UNESP], Costa, Kelton A. P [UNESP], Pereira, Danillo R., Passos, Leandro A.
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1109/ICPR56361.2022.9956112; http://hdl.handle.net/11449/249455
• topic: Computer Networks Security; Feature Selection; Finite Element Method; Machine Learning

Identifying anomalies has become one of the primary strategies towards security and protection procedures in computer networks. In this context, machine learning-based methods emerge as an elegant solution to identify such scenarios and learn irrelevant information so that a reduction in the identification time and possible gain in accuracy can be obtained. This paper proposes a novel feature selection approach called Finite Element Machines for Feature Selection (FEMa-FS), which uses the framework of finite elements to identify the most relevant information from a given dataset. Although FEMa-FS can be applied to any application domain, it has been evaluated in the context of anomaly detection in computer networks. The outcomes over two datasets showed promising results.