FEMa-FS: Finite Element Machines for Feature Selection
This work addresses anomaly detection for computer network security, presenting an incremental method for feature selection.
The paper tackles the problem of identifying anomalies in computer networks by proposing FEMa-FS, a novel feature selection approach using finite elements, which showed promising results in evaluations on two datasets.
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.