A Hybrid Approach to Malware Detection: Integrating Few-Shot Model-Agnostic Meta-Learning with Autoencoders
This research proposes a hybrid model for few-shot malware detection by combining an Autoencoder Feature Extractor with Model-Agnostic Meta-Learning (MAML). The autoencoder learns compact, unsupervised representations of malware features from a large dataset. MAML then uses these representations to rapidly adapt to new, unseen malware classes using very little labeled data. This approach allows the system to quickly recognize novel threats without extensive retraining.