2024
DOI: 10.3390/info15050262
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Novel Ransomware Detection Exploiting Uncertainty and Calibration Quality Measures Using Deep Learning

Mazen Gazzan,
Frederick T. Sheldon

Abstract: Ransomware poses a significant threat by encrypting files or systems demanding a ransom be paid. Early detection is essential to mitigate its impact. This paper presents an Uncertainty-Aware Dynamic Early Stopping (UA-DES) technique for optimizing Deep Belief Networks (DBNs) in ransomware detection. UA-DES leverages Bayesian methods, dropout techniques, and an active learning framework to dynamically adjust the number of epochs during the training of the detection model, preventing overfitting while enhancing … Show more

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