Ransomware detection using stacked autoencoder for feature selection

arXiv:2402.11342v1 Announce Type: new
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 …

abstract accuracy advanced aim arxiv autoencoder classification classifier cs.cr cs.lg detection feature feature selection lstm memory processing ransomware study type