Adapter-Based Few-Shot Continual Learning for Malicious Packet Recognition
The continual evolution of malware variants necessitates detection systems that can adapt to new threats without retraining from scratch. However, continually updating models on new data often leads to catastrophic forgetting, where previously learned knowledge is overwritten. While continual learning has been increasingly explored for malware detection, the specific setting of Few-Shot Class-Incremental Learning (FSCIL), where new malware classes must be learned from only a small number of labeled examples, remains comparatively underexplored. Therefore, this work investigates the FSCIL setting for malware classification. To address the stability-plasticity dilemma, we propose a hybrid f...
| Authors | Kyle Stein, Guillermo Francia, III Eman El-Sheikh et al. |
|---|---|
| Primary category | cs.CR |
| Published | 24 August 2026 |
| Last updated at source | 24 August 2026 |
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