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On Detecting Cryptojacking on Websites: Revisiting the Use of Classifiers

Cryptojacking or illegal mining is a form of malware that hides in the victim’s computer and takes the computational resources to extract cryptocurrencies in favor of the attacker. It generates significant computational consumption, reducing the computational efficiency of the victim’s computer. Thi...

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Autores principales: Aponte-Novoa, Fredy Andrés, Povedano Álvarez, Daniel, Villanueva-Polanco, Ricardo, Sandoval Orozco, Ana Lucila, García Villalba, Luis Javier
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9740044/
https://www.ncbi.nlm.nih.gov/pubmed/36501921
http://dx.doi.org/10.3390/s22239219
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author Aponte-Novoa, Fredy Andrés
Povedano Álvarez, Daniel
Villanueva-Polanco, Ricardo
Sandoval Orozco, Ana Lucila
García Villalba, Luis Javier
author_facet Aponte-Novoa, Fredy Andrés
Povedano Álvarez, Daniel
Villanueva-Polanco, Ricardo
Sandoval Orozco, Ana Lucila
García Villalba, Luis Javier
author_sort Aponte-Novoa, Fredy Andrés
collection PubMed
description Cryptojacking or illegal mining is a form of malware that hides in the victim’s computer and takes the computational resources to extract cryptocurrencies in favor of the attacker. It generates significant computational consumption, reducing the computational efficiency of the victim’s computer. This attack has increased due to the rise of cryptocurrencies and their profitability and its difficult detection by the user. The identification and blocking of this type of malware have become an aspect of research related to cryptocurrencies and blockchain technology; in the literature, some machine learning and deep learning techniques are presented, but they are still susceptible to improvement. In this work, we explore multiple Machine Learning classification models for detecting cryptojacking on websites, such as Logistic Regression, Decision Tree, Random Forest, Gradient Boosting Classifier, k-Nearest Neighbor, and XGBoost. To this end, we make use of a dataset, composed of network and host features’ samples, to which we apply various feature selection methods such as those based on statistical methods, e.g., Test Anova, and other methods as Wrappers, not only to reduce the complexity of the built models but also to discover the features with the greatest predictive power. Our results suggest that simple models such as Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, and k-Nearest Neighbor models, can achieve success rate similar to or greater than that of advanced algorithms such as XGBoost and even those of other works based on Deep Learning.
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spelling pubmed-97400442022-12-11 On Detecting Cryptojacking on Websites: Revisiting the Use of Classifiers Aponte-Novoa, Fredy Andrés Povedano Álvarez, Daniel Villanueva-Polanco, Ricardo Sandoval Orozco, Ana Lucila García Villalba, Luis Javier Sensors (Basel) Article Cryptojacking or illegal mining is a form of malware that hides in the victim’s computer and takes the computational resources to extract cryptocurrencies in favor of the attacker. It generates significant computational consumption, reducing the computational efficiency of the victim’s computer. This attack has increased due to the rise of cryptocurrencies and their profitability and its difficult detection by the user. The identification and blocking of this type of malware have become an aspect of research related to cryptocurrencies and blockchain technology; in the literature, some machine learning and deep learning techniques are presented, but they are still susceptible to improvement. In this work, we explore multiple Machine Learning classification models for detecting cryptojacking on websites, such as Logistic Regression, Decision Tree, Random Forest, Gradient Boosting Classifier, k-Nearest Neighbor, and XGBoost. To this end, we make use of a dataset, composed of network and host features’ samples, to which we apply various feature selection methods such as those based on statistical methods, e.g., Test Anova, and other methods as Wrappers, not only to reduce the complexity of the built models but also to discover the features with the greatest predictive power. Our results suggest that simple models such as Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, and k-Nearest Neighbor models, can achieve success rate similar to or greater than that of advanced algorithms such as XGBoost and even those of other works based on Deep Learning. MDPI 2022-11-27 /pmc/articles/PMC9740044/ /pubmed/36501921 http://dx.doi.org/10.3390/s22239219 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Aponte-Novoa, Fredy Andrés
Povedano Álvarez, Daniel
Villanueva-Polanco, Ricardo
Sandoval Orozco, Ana Lucila
García Villalba, Luis Javier
On Detecting Cryptojacking on Websites: Revisiting the Use of Classifiers
title On Detecting Cryptojacking on Websites: Revisiting the Use of Classifiers
title_full On Detecting Cryptojacking on Websites: Revisiting the Use of Classifiers
title_fullStr On Detecting Cryptojacking on Websites: Revisiting the Use of Classifiers
title_full_unstemmed On Detecting Cryptojacking on Websites: Revisiting the Use of Classifiers
title_short On Detecting Cryptojacking on Websites: Revisiting the Use of Classifiers
title_sort on detecting cryptojacking on websites: revisiting the use of classifiers
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9740044/
https://www.ncbi.nlm.nih.gov/pubmed/36501921
http://dx.doi.org/10.3390/s22239219
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