| | --- |
| | 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 |
