  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.
