On detecting cryptojacking on websites: revisiting the use of classifiers

HIGHLIGHTS

• who: Fredy Andres Aponte-Novoa and colleagues from the Department of Computer Science and Engineering, Universidad del Norte, Barranquilla, Colombia have published the Article: On Detecting Cryptojacking on Websites: Revisiting the Use of Classifiers, in the Journal: Sensors 2022, 22, 9219. of /2022/
• what: The authors explore multiple Machine Learning classification models for detecting cryptojacking websites such as Logistic Regression Decision Tree Random Forest Gradient Boosting Classifier k-Nearest Neighbor and XGBoost. The authors follow a methodology, whose phases are shown in Figure 1 and described next. Method For the cross-validation and evaluation of . . .
  

   

  

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