FEMa-FS: Finite Element Machines for Feature Selection

TL;DR

FEMa-FS introduces a finite element-based feature selection method to improve anomaly detection in computer networks, demonstrating promising results on two datasets.

Contribution

The paper presents a novel finite element machine framework for feature selection, applicable across domains with a focus on network anomaly detection.

Findings

Effective in identifying relevant features for anomaly detection

Reduces identification time and improves accuracy

Validated on two network datasets

Abstract

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.