Performance of Machine Learning-Based Multi-Model Voting Ensemble Methods for Network Threat Detection in Agriculture 4.0

The upcoming agricultural revolution, known as Agriculture 4.0, integrates cutting-edge Information and Communication Technologies in existing operations. Various cyber threats related to the aforementioned integration have attracted increasing interest from security researchers. Network traffic ana...

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Autores principales:	Nikolaos Peppes, Emmanouil Daskalakis, Theodoros Alexakis, Evgenia Adamopoulou, Konstantinos Demestichas
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	machine learning network traffic classification voting ensemble network threats network security intrusion detection Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/d6fbf10f090e4c2faf9c196a0f9d6a44
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spelling	oai:doaj.org-article:d6fbf10f090e4c2faf9c196a0f9d6a442021-11-25T18:56:45ZPerformance of Machine Learning-Based Multi-Model Voting Ensemble Methods for Network Threat Detection in Agriculture 4.010.3390/s212274751424-8220https://doaj.org/article/d6fbf10f090e4c2faf9c196a0f9d6a442021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/22/7475https://doaj.org/toc/1424-8220The upcoming agricultural revolution, known as Agriculture 4.0, integrates cutting-edge Information and Communication Technologies in existing operations. Various cyber threats related to the aforementioned integration have attracted increasing interest from security researchers. Network traffic analysis and classification based on Machine Learning (ML) methodologies can play a vital role in tackling such threats. Towards this direction, this research work presents and evaluates different ML classifiers for network traffic classification, i.e., K-Nearest Neighbors (KNN), Support Vector Classification (SVC), Decision Tree (DT), Random Forest (RF) and Stochastic Gradient Descent (SGD), as well as a hard voting and a soft voting ensemble model of these classifiers. In the context of this research work, three variations of the NSL-KDD dataset were utilized, i.e., initial dataset, undersampled dataset and oversampled dataset. The performance of the individual ML algorithms was evaluated in all three dataset variations and was compared to the performance of the voting ensemble methods. In most cases, both the hard and the soft voting models were found to perform better in terms of accuracy compared to the individual models.Nikolaos PeppesEmmanouil DaskalakisTheodoros AlexakisEvgenia AdamopoulouKonstantinos DemestichasMDPI AGarticlemachine learningnetwork traffic classificationvoting ensemblenetwork threatsnetwork securityintrusion detectionChemical technologyTP1-1185ENSensors, Vol 21, Iss 7475, p 7475 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	machine learning network traffic classification voting ensemble network threats network security intrusion detection Chemical technology TP1-1185
spellingShingle	machine learning network traffic classification voting ensemble network threats network security intrusion detection Chemical technology TP1-1185 Nikolaos Peppes Emmanouil Daskalakis Theodoros Alexakis Evgenia Adamopoulou Konstantinos Demestichas Performance of Machine Learning-Based Multi-Model Voting Ensemble Methods for Network Threat Detection in Agriculture 4.0
description	The upcoming agricultural revolution, known as Agriculture 4.0, integrates cutting-edge Information and Communication Technologies in existing operations. Various cyber threats related to the aforementioned integration have attracted increasing interest from security researchers. Network traffic analysis and classification based on Machine Learning (ML) methodologies can play a vital role in tackling such threats. Towards this direction, this research work presents and evaluates different ML classifiers for network traffic classification, i.e., K-Nearest Neighbors (KNN), Support Vector Classification (SVC), Decision Tree (DT), Random Forest (RF) and Stochastic Gradient Descent (SGD), as well as a hard voting and a soft voting ensemble model of these classifiers. In the context of this research work, three variations of the NSL-KDD dataset were utilized, i.e., initial dataset, undersampled dataset and oversampled dataset. The performance of the individual ML algorithms was evaluated in all three dataset variations and was compared to the performance of the voting ensemble methods. In most cases, both the hard and the soft voting models were found to perform better in terms of accuracy compared to the individual models.
format	article
author	Nikolaos Peppes Emmanouil Daskalakis Theodoros Alexakis Evgenia Adamopoulou Konstantinos Demestichas
author_facet	Nikolaos Peppes Emmanouil Daskalakis Theodoros Alexakis Evgenia Adamopoulou Konstantinos Demestichas
author_sort	Nikolaos Peppes
title	Performance of Machine Learning-Based Multi-Model Voting Ensemble Methods for Network Threat Detection in Agriculture 4.0
title_short	Performance of Machine Learning-Based Multi-Model Voting Ensemble Methods for Network Threat Detection in Agriculture 4.0
title_full	Performance of Machine Learning-Based Multi-Model Voting Ensemble Methods for Network Threat Detection in Agriculture 4.0
title_fullStr	Performance of Machine Learning-Based Multi-Model Voting Ensemble Methods for Network Threat Detection in Agriculture 4.0
title_full_unstemmed	Performance of Machine Learning-Based Multi-Model Voting Ensemble Methods for Network Threat Detection in Agriculture 4.0
title_sort	performance of machine learning-based multi-model voting ensemble methods for network threat detection in agriculture 4.0
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/d6fbf10f090e4c2faf9c196a0f9d6a44
work_keys_str_mv	AT nikolaospeppes performanceofmachinelearningbasedmultimodelvotingensemblemethodsfornetworkthreatdetectioninagriculture40 AT emmanouildaskalakis performanceofmachinelearningbasedmultimodelvotingensemblemethodsfornetworkthreatdetectioninagriculture40 AT theodorosalexakis performanceofmachinelearningbasedmultimodelvotingensemblemethodsfornetworkthreatdetectioninagriculture40 AT evgeniaadamopoulou performanceofmachinelearningbasedmultimodelvotingensemblemethodsfornetworkthreatdetectioninagriculture40 AT konstantinosdemestichas performanceofmachinelearningbasedmultimodelvotingensemblemethodsfornetworkthreatdetectioninagriculture40
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Performance of Machine Learning-Based Multi-Model Voting Ensemble Methods for Network Threat Detection in Agriculture 4.0

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