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On Detecting Cryptojacking on Websites: Revisiting the Use of Classifiers.

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Area of Science:

  • Cybersecurity
  • Machine Learning
  • Blockchain Technology

Background:

  • Cryptojacking involves malware secretly using a victim's computational resources for cryptocurrency mining.
  • The profitability of cryptocurrencies has led to a rise in cryptojacking attacks, which are often undetected by users.
  • Detecting and blocking cryptojacking is a growing research area within cryptocurrency and blockchain technology.

Purpose of the Study:

  • To explore and evaluate multiple Machine Learning (ML) classification models for detecting cryptojacking on websites.
  • To identify the most effective features for predicting cryptojacking using feature selection methods.
  • To compare the performance of various ML models against each other and against existing Deep Learning approaches.

Main Methods:

  • Utilized a dataset comprising network and host features for cryptojacking detection.
  • Applied feature selection techniques, including statistical methods (e.g., Test Anova) and wrapper methods, to reduce model complexity and identify predictive features.
  • Trained and evaluated several ML classification models: Logistic Regression, Decision Tree, Random Forest, Gradient Boosting Classifier, k-Nearest Neighbor, and XGBoost.

Main Results:

  • Simple ML models, including Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, and k-Nearest Neighbor, demonstrated high success rates in detecting cryptojacking.
  • These simpler models achieved performance comparable to or better than advanced algorithms like XGBoost.
  • The findings indicate that basic ML models can be as effective, or even more so, than complex Deep Learning methods in cryptojacking detection.

Conclusions:

  • Machine learning offers effective solutions for identifying cryptojacking threats.
  • Simpler machine learning models provide a viable and efficient alternative to complex algorithms for cryptojacking detection.
  • Further research can build upon these findings to enhance the security of web users against cryptojacking.