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

1. SSH-Bruteforce (patator) record from original dataset
2. SSH-Bruteforce (patator) record from replicated attack dataset
3. Slowloris DoS record from original dataset
4. Slowloris DoS record from replicated attack dataset
5. GoldenEye DoS record from original dataset
6. 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