Adapter-Based Few-Shot Continual Learning for Malicious Packet Recognition

The paper addresses the challenge of adapting malware detection systems to new threats without retraining from scratch, focusing on the Few-Shot Class-Incremental Learning (FSCIL) setting. It proposes a hybrid framework that uses a self-supervised learning backbone pre-trained on malware packets, incorporates Low-Rank Adaptation (LoRA) to adapt the model while preserving core representations, and employs a prototype-based classification head for incremental sessions. Experiments on multiple datasets show that this approach consistently outperforms existing FSCIL baselines and achieves state-of-the-art performance.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

• fine-tuning
• benchmarks