Ensemble learning techniques for intrusion detection system in the context of cybersecurity

TL;DR

This paper investigates the use of ensemble learning, specifically stacking with SVM and kNN, to improve DDoS attack detection in intrusion detection systems within cybersecurity.

Contribution

It introduces a novel ensemble approach combining SVM and kNN for enhanced intrusion detection performance using the stacking method.

Findings

Improved detection accuracy for DDoS attacks.

Enhanced performance of IDS with ensemble learning.

Effective use of Data Mining and Machine Learning tools.

Abstract

Recently, there has been an interest in improving the resources available in Intrusion Detection System (IDS) techniques. In this sense, several studies related to cybersecurity show that the environment invasions and information kidnapping are increasingly recurrent and complex. The criticality of the business involving operations in an environment using computing resources does not allow the vulnerability of the information. Cybersecurity has taken on a dimension within the universe of indispensable technology in corporations, and the prevention of risks of invasions into the environment is dealt with daily by Security teams. Thus, the main objective of the study was to investigate the Ensemble Learning technique using the Stacking method, supported by the Support Vector Machine (SVM) and k-Nearest Neighbour (kNN) algorithms aiming at an optimization of the results for DDoS attack detection. For this, the Intrusion Detection System concept was used with the application of the Data Mining and Machine Learning Orange tool to obtain better results