Advanced Network Detection Systems are important to detect established and unknown network attacks, as traditional signature-based methods fail against novel threats and supervised machine learning requires extensive labeled data for new attacks. This study proposes a hybrid deep learning approach: an unsupervised Variational Autoencoder (VAE) for anomaly detection, coupled with a GNN-enhanced Few-Shot Learning (FSL) classifier. The VAE, trained solely on 'Normal' data from the UNSW-NB15 dataset, identifies anomalies using a high-percentile reconstruction error threshold. Subsequently, a Prototypical Network (ProtoNet), as the FSL classifier, is episodically trained on 'Normal' data and a select subset of previously seen attack types to classify these anomalies. Using these, the malware through network traffic is prioritized for newer variants using the few shot component, after the VAE detects the first stage.
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