Network Intrusion Detection Based on Data Feature Dynamic Ensemble Model

Feng, Tao; Dou, Manfang; Xie, Peng; Fang, Junli

doi:10.1007/978-981-15-8086-4_62

Cited by 1 publication

(1 citation statement)

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“…Feng et al [108] designed a two-layer integration model based on the stacking framework. The researchers tested different classifiers and ensemble methods to determine which had better performance and if the integration model improved the quality of the results.…”

Section: F Work Published In 2020mentioning

confidence: 99%

A Comprehensive Survey on Ensemble Learning-Based Intrusion Detection Approaches in Computer Networks

Lucas,

de Figueiredo,

Tojeiro

et al. 2023

IEEE Access

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Machine learning algorithms present a robust alternative for building Intrusion Detection Systems due to their ability to recognize attacks in computer network traffic by recognizing patterns in large amounts of data. Typically, classifiers are trained for this task. Together, ensemble learning algorithms have increased the performance of these detectors, reducing classification errors and allowing computer networks to be more protected. This research presents a comprehensive Systematic Review of the Literature where works related to intrusion detection with ensemble learning were obtained from the most relevant scientific bases. We offer 188 works, several compilations of datasets, classifiers, and ensemble algorithms, and document the experiments that stood out in their performance. A characteristic of this research is its originality. We found two surveys in the literature specifically focusing on the relationship between ensemble techniques and intrusion detection. We present for the last eight years covered by this survey a timeline-based view of the works studied to highlight evolutions and trends. The results obtained by our survey show a growing area, with excellent results in detecting attacks but with needs for improvement in pruning for choosing classifiers, which makes this work unprecedented for this context.

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Section: F Work Published In 2020mentioning

confidence: 99%