  Zero-day attack detection plays a critical role in mitigating risks,
protecting assets, and staying ahead in the evolving threat landscape. This
study explores the application of stacked autoencoder (SAE), a type of
artificial neural network, for feature selection and zero-day threat
classification using a Long Short-Term Memory (LSTM) scheme. The process
involves preprocessing the UGRansome dataset and training an unsupervised SAE
for feature extraction. Finetuning with supervised learning is then performed
to enhance the discriminative capabilities of this model. The learned weights
and activations of the autoencoder are analyzed to identify the most important
features for discriminating between zero-day threats and normal system
behavior. These selected features form a reduced feature set that enables
accurate classification. The results indicate that the SAE-LSTM performs well
across all three attack categories by showcasing high precision, recall, and F1
score values, emphasizing the model's strong predictive capabilities in
identifying various types of zero-day attacks. Additionally, the balanced
average scores of the SAE-LSTM suggest that the model generalizes effectively
and consistently across different attack categories.
