---
license: mit
---

# netFound-small

## Description

netFound is a network traffic encoder model that uses transformer architecture and includes a pretraining phase on unlabeled data to achieve high results.  

Key features:
- netFound takes raw PCAP data as input
- netFound can (and need) be pretrained on the unlabeled dataset
- netFound uses Hierarchical Transformer architecture to take into account packet burst and flow behavior
- netFound uses burst metadata (inter arrival time, number of bytes per burst, etc)

## Details
- Model config: small (4 layers, 4 attention heads, 512 hidden size)
- Pretraining details: 128 GPUs, 45k steps, ~9bln tokens seen

## Source code

https://github.com/SNL-UCSB/netFound