FreeMOCA: Memory-Free Continual Learning for Malicious Code Analysis
As over 200 million new malware samples are identified each year, antivirus systems must continuously adapt to the evolving threat landscape. However, retraining solely on new samples leads to catastrophic forgetting and exploitable blind spots, while retraining on the entire dataset incurs substantial computational cost. We propose FreeMOCA, a memory- and compute-efficient continual learning framework for malicious code analysis that preserves prior knowledge via adaptive layer-wise interpolation between consecutive task updates, leveraging the fact that warm-started task optima are connected by low-loss paths in parameter space. We evaluate FreeMOCA in both class-incremental (Class-IL) ...
| Authors | Zahra Asadi, Haeseung Jeon, Sohyun Han et al. |
|---|---|
| Primary category | cs.CR |
| Published | 10 May 2026 |
| Last updated at source | 14 May 2026 |
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