Ransomware detection using stacked autoencoder for feature selection
Mike Nkongolo Mahmut Tokmak
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 meticulously
analyzes the autoencoder's learned weights and activations to identify
essential features for distinguishing ransomware families from other malware
and creates a streamlined feature set for precise classification. Extensive
experiments, including up to 400 epochs and varying learning rates, are
conducted to optimize the model's performance. The results demonstrate the
outstanding performance of the SAE-LSTM model across all ransomware families,
boasting high precision, recall, and F1 score values that underscore its robust
classification capabilities. Furthermore, balanced average scores affirm the
proposed model's ability to generalize effectively across various malware
types. The proposed model achieves an exceptional 99% accuracy in ransomware
classification, surpassing the Extreme Gradient Boosting (XGBoost) algorithm
primarily due to its effective SAE feature selection mechanism. The model also
demonstrates outstanding performance in identifying signature attacks,
achieving a 98% accuracy rate.