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New Machine Learning Model Improves Cryptojacking Detection

/ 1 min read

🧠💻✨ New machine learning model enhances detection of cryptojacking threats. A recent study has developed a semi-supervised machine learning approach to detect cryptojacking, the unauthorized use of computing resources for cryptocurrency mining. Utilizing an autoencoder for feature extraction and a random forest model for classification, the methodology aims to improve detection accuracy while ensuring interpretability. The model is further enhanced with explainable AI techniques, such as LIME, to clarify predictions. Results from datasets like UGRansome and BitcoinHeist show accuracy rates between 70% and 99%, indicating that this approach offers an efficient and scalable solution for real-time cryptojacking detection across various scenarios, addressing a growing concern in the digital landscape.

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