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metadata
license: mit
base_model: roberta-large
tags:
  - generated_from_trainer
metrics:
  - f1
model-index:
  - name: bert-unformatted-network-data-test-ids-2018
    results: []
widget:
  - text: >-
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      0.0 0 0 0.0 0.0 0 0 0 0 0 0 86.9090909091 121.1363636364 0 22 22 1912 2665
    example_title: 1 SSH
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    example_title: 2 SSH recreated
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    example_title: 3 Slowloris
  - text: >-
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      357.9098067287044 3 2 379 140 239 66 126.33333333333331 79.73428093082394
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    example_title: 4 Slowloris recreated
  - text: >-
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    example_title: 5 GoldenEye
  - text: >-
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      0.0 0.0 0.0 0.0 0 0 0 0 0 0 66.0 66.0 0 2 1 132 66
    example_title: 6 GoldenEye recreated

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