Ransomware detection using stacked autoencoder for feature selection

arXiv:2402.11342v1 Announce Type: cross
Abstract: The aim of this study is to propose and evaluate an advanced ransomware detection and classification method that combines a Stacked Autoencoder (SAE) for precise feature selection with a Long Short Term Memory (LSTM) classifier to enhance ransomware stratification accuracy. The proposed approach involves thorough pre processing of the UGRansome dataset and training an unsupervised SAE for optimal feature selection or fine tuning via supervised learning to elevate the LSTM model's classification capabilities. The study …

accuracy advanced aim arxiv classification cs.cr cs.lg detection feature memory ransomware ransomware detection study