PLUME: Building a Network-Native Foundation Model for Wireless Traces via Protocol-Aware Tokenization
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arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866915862177382400 |
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| author | Pradhan, Swadhin Irshad, Shazal Henry, Jerome |
| author_facet | Pradhan, Swadhin Irshad, Shazal Henry, Jerome |
| contents | Foundation models succeed when they learn in the native structure of a modality, whether morphology-respecting tokens in language or pixels in vision. Wireless packet traces deserve the same treatment: meaning emerges from layered headers, typed fields, timing gaps, and cross-packet state machines, not flat strings. We present Plume (Protocol Language Understanding Model for Exchanges), a compact 140M-parameter foundation model for 802.11 traces that learns from structured PDML dissections. A protocol-aware tokenizer splits along the dissector field tree, emits gap tokens for timing, and normalizes identifiers, yielding 6.2x shorter sequences than BPE with higher per token information density. Trained on a curated corpus, Plume achieves 74-97% next-packet token accuracy across five real-world failure categories and AUROC >= 0.99 for zero-shot anomaly detection. On the same prediction task, frontier LLMs (Claude Opus 4.6, GPT-5.4) score comparably despite receiving identical protocol context, yet Plume does so with > 600x fewer parameters, fitting on a single GPU at effectively zero marginal cost vs. cloud API pricing, enabling on-prem, privacy-preserving root cause analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_13647 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | PLUME: Building a Network-Native Foundation Model for Wireless Traces via Protocol-Aware Tokenization Pradhan, Swadhin Irshad, Shazal Henry, Jerome Machine Learning Networking and Internet Architecture 68T07, 68M10, 94A05 C.2.1; C.2.3; I.2.6 Foundation models succeed when they learn in the native structure of a modality, whether morphology-respecting tokens in language or pixels in vision. Wireless packet traces deserve the same treatment: meaning emerges from layered headers, typed fields, timing gaps, and cross-packet state machines, not flat strings. We present Plume (Protocol Language Understanding Model for Exchanges), a compact 140M-parameter foundation model for 802.11 traces that learns from structured PDML dissections. A protocol-aware tokenizer splits along the dissector field tree, emits gap tokens for timing, and normalizes identifiers, yielding 6.2x shorter sequences than BPE with higher per token information density. Trained on a curated corpus, Plume achieves 74-97% next-packet token accuracy across five real-world failure categories and AUROC >= 0.99 for zero-shot anomaly detection. On the same prediction task, frontier LLMs (Claude Opus 4.6, GPT-5.4) score comparably despite receiving identical protocol context, yet Plume does so with > 600x fewer parameters, fitting on a single GPU at effectively zero marginal cost vs. cloud API pricing, enabling on-prem, privacy-preserving root cause analysis. |
| title | PLUME: Building a Network-Native Foundation Model for Wireless Traces via Protocol-Aware Tokenization |
| topic | Machine Learning Networking and Internet Architecture 68T07, 68M10, 94A05 C.2.1; C.2.3; I.2.6 |
| url | https://arxiv.org/abs/2603.13647 |