LATIDIA · Ciberseguridad

Evaluación empírica del aprendizaje automático para la detección de criptojacking en la nube: efectos del preprocesamiento y los protocolos de evaluación

arXiv: 2610.20658v1Tipo de anuncio: nuevo Resumen: La inteligencia artificial (IA) se ha aplicado ampliamente a la detección de cryptojacking en la nube. Los modelos clásicos de aprendizaje automático siguen siendo uno de los enfoques más utilizados para

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arXiv:2610.20658v1 Announce Type: new Abstract: Artificial intelligence (AI) has been widely applied to cloud cryptojacking detection. Classical machine learning models remain among the most widely used approaches for cloud cryptojacking detection. This paper presents an empirical evaluation of six widely used classical machine learning models for AI-based cloud cryptojacking detection under different preprocessing and evaluation settings. Because no public cloud-native cryptojacking dataset is currently available, experiments were conducted using two publicly available proxy datasets, DS2OS and NSL-KDD. The evaluated models include Random Forest, XGBoost, LightGBM, Gradient Boosting, Decision Tree, and K-Nearest Neighbors (KNN). Experimental results show that preprocessing choices and evaluation protocols have a measurable impact on model performance. On