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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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. |
format | Online Article Text |
id | pubmed-9740044 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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