MambaNetBurst: Direct Byte-level Network Traffic Classification without Tokenization or Pretraining
We present MambaNetBurst, a compact tokenizer-free byte-level sequence classifier for network burst classification based on a Mamba-2 backbone. In contrast to most recent strong traffic-classification and intrusion-detection approaches, our method operates directly on raw packet bytes, avoids tokenization, patching, and heavy engineered multimodal representations, and does not require any self-supervised pre-training stage. Given a packet flow, we form a fixed-length burst from the first few packets, embed the resulting byte sequence appending a learnable CLS token, and process it with a stack of residual pre-normalized Mamba-2 blocks for end-to-end supervised classification. Across six p...
| Authors | Gayan K. Kulatilleke, Siamak Layeghy, Mahsa Baktashmotlagh et al. |
|---|---|
| Primary category | cs.CR |
| Published | 11 May 2026 |
| Last updated at source | 11 May 2026 |
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