On Detecting Cryptojacking on Websites: Revisiting the Use of Classifiers

【Author】 Aponte-Novoa, Fredy Andres; Povedano alvarez, Daniel; Villanueva-Polanco, Ricardo; Sandoval Orozco, Ana Lucila; Garcia Villalba, Luis Javier

【Source】SENSORS

【影响因子】3.847

【Abstract】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.

【Keywords】blockchain; cryptojacking; illegal mining; malware; machine learning

【发表时间】2022 DEC

【收录时间】2023-01-04

【文献类型】理论模型

【主题类别】

区块链治理-技术治理-挖矿检测