bert-unformatted-network-data-test-ids-2018
This model is a fine-tuned version of roberta-large on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0000
- F1: 1.0
EXAMPLE FULL NAMES:
'Benign': label_0, 'SSH-Bruteforce': label_1, 'DoS attacks-Slowloris': label_2, 'DoS attacks-GoldenEye': label_3
- SSH-Bruteforce (patator) record from original dataset
- SSH-Bruteforce (patator) record from replicated attack dataset
- Slowloris DoS record from original dataset
- Slowloris DoS record from replicated attack dataset
- GoldenEye DoS record from original dataset
- GoldenEye DoS record from replicated attack dataset
examples from CSE-CIC-IDS2018 on AWS (formatted for model training) https://colab.research.google.com/drive/1PmLep9D3NfMhYsX0soTBhfVXFkawGgGx?authuser=0#scrollTo=ReaH6NCljdsn
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| 0.0033 | 1.0 | 1500 | 0.0000 | 1.0 |
| 0.0038 | 2.0 | 3000 | 0.0000 | 1.0 |
| 0.0 | 3.0 | 4500 | 0.0000 | 1.0 |
Framework versions
- Transformers 4.42.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
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Model tree for Jios/bert-unformatted-network-data-test-ids-2018
Base model
FacebookAI/roberta-large