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--- |
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license: mit |
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base_model: roberta-large |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: bert-unformatted-network-data-test |
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results: [] |
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widget: |
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- text: "foreign ip address 58793 local ip address 36639 17 1 2 0 750 0 375 375 375.0 0.0 0 0 0.0 0.0 750000000.0 2000000.0 1.0 0.0 1 1 1 1.0 0.0 1 1 0 0.0 0.0 0 0 0 0 0 0 -2 0 2000000.0 0.0 375 375 375.0 0.0 0.0 0 0 0 0 0 0 0 0 0 562.5 375.0 0.0 -2 0 0 0 0 0 0 2 750 0 0 -1 -1 1 -1 0.0 0.0 0 0 0.0 0.0 0 0 0 1" |
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example_title: "1. malicious from training dataset" |
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- text: "local ip address 59665 foreign ip address 443 6 3 2 0 12.0 0.0 6.0 6.0 6.0 0.0 0.0 0.0 0.0 0.0 4000000.0 666666.6666666666 3.0 0.0 3.0 3.0 3.0 3.0 0.0 3.0 3.0 0.0 0.0 0.0 0.0 0.0 0 0 0 0 40 0 666666.6666666665 0.0 6.0 6.0 6.0 0.0 0.0 0 0 0 0 0 1 0 0 0.0 9.0 6.0 0.0 40 0 0 0 0 0 0 2 12 0 0 16247 -1 1 20 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 0" |
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example_title: "2. benign from training dataset" |
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- text: "foreign ip address 36378 local ip address 27243 17 105402 4 0 1438 0 389 330 359.5 34.06366588218791 0 0 0.0 0.0 13643.004876567808 37.949944023832565 35134.0 60813.17404148545 105355 1 105402 35134.0 60813.17404148545 105355 1 0 0.0 0.0 0 0 0 0 0 0 -4 0 37.94994402383257 0.0 330 389 353.6 32.31563089 1044.3 0 0 0 0 0 0 0 0 0 442.0 359.5 0.0 -4 0 0 0 0 0 0 4 1438 0 0 -1 -1 3 -1 0.0 0.0 0 0 0.0 0.0 0 0 0 1" |
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example_title: "3. malicious outside training dataset" |
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- text: "foreign ip address 49566 local ip address 21279 17 212617 6 0 2088 0 393 321 348.0 35.08845964131227 0 0 0.0 0.0 9820.475315 28.21975665163181 42523.4 58283.99558 109977 1 212617 42523.4 58283.99558 109977 1 0 0.0 0.0 0 0 0 0 0 0 -6 0 28.21975665163181 0.0 321 393 344.1428571428572 33.61759743263722 1130.1428571428569 0 0 0 0 0 0 0 0 0 401.5 348.0 0.0 -6 0 0 0 0 0 0 6 2088 0 0 -1 -1 5 -1 0.0 0.0 0 0 0.0 0.0 0 0 0 1" |
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example_title: "4. malicious outside training dataset 2" |
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- text: "foreign ip address 52280 local ip address 14896 17 108934 4 0 1398 0 369 330 349.5 22.516660498395403 0 0 0.0 0.0 12833.458791561863 36.71948152092092 36311.33333333333 62890.47614967893 108931 1 108934 36311.33333333333 62890.47614967893 108931 1 0 0.0 0.0 0 0 0 0 0 0 -4 0 36.71948152092092 0.0 330 369 345.6 21.36117974270148 456.3 0 0 0 0 0 0 0 0 0 432.0 349.5 0.0 -4 0 0 0 0 0 0 4 1398 0 0 -1 -1 3 -1 0.0 0.0 0 0 0.0 0.0 0 0 0 1" |
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example_title: "5. benign outside training dataset" |
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- text: "local ip address 59316 foreign ip address 53 17 20902 2 2 102.0 134.0 51.0 51.0 51.0 0.0 67.0 67.0 67.0 0.0 11290.78557075878 191.3692469620132 6967.333333333334 12063.445209944515 20897.0 2.0 3.0 3.0 0.0 3.0 3.0 2.0 2.0 0.0 2.0 2.0 0 0 0 0 64 64 95.6846234810066 95.6846234810066 51.0 67.0 57.4 8.763560920082657 76.8 0 0 0 0 0 0 0 0 1.0 71.75 51.0 67.0 64 0 0 0 0 0 0 2 102 2 134 -1 -1 1 32 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 0" |
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example_title: "6. benign outside training dataset 2" |
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- text: "local ip address 58891 foreign ip address 53 17 20849 2 2 82.0 114.0 41.0 41.0 41.0 0.0 57.0 57.0 57.0 0.0 9400.9305002638 191.85572449517963 6949.666666666666 12032.84564570382 20844.0 2.0 3.0 3.0 0.0 3.0 3.0 2.0 2.0 0.0 2.0 2.0 0 0 0 0 40 40 95.9278622475898 95.9278622475898 41.0 57.0 47.4 8.763560920082657 76.8 0 0 0 0 0 0 0 0 1.0 59.25 41.0 57.0 40 0 0 0 0 0 0 2 82 2 114 -1 -1 1 20 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 0 foreign ip address 34237 local ip address 30461 17 2 2 0 766 0 383 383 383.0 0.0 0 0 0.0 0.0 383000000.0 1000000.0 2.0 0.0 2 2 2 2.0 0.0 2 2 0 0.0 0.0 0 0 0 0 0 0 -2 0 1000000.0 0.0 383 383 383.0 0.0 0.0 0 0 0 0 0 0 0 0 0 574.5 383.0 0.0 -2 0 0 0 0 0 0 2 766 0 0 -1 -1 1 -1 0.0 0.0 0 0 0.0 0.0 0 0 0 1" |
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example_title: "7. benign then malicious same entry" |
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- text: "local ip address 60647 foreign ip address 53 17 20726 2 2 80.0 112.0 40.0 40.0 40.0 0.0 56.0 56.0 56.0 0.0 9263.726720061755 192.99430666795328 6908.666666666666 11961.831562655168 20721.0 2.0 3.0 3.0 0.0 3.0 3.0 2.0 2.0 0.0 2.0 2.0 0 0 0 0 40 40 96.49715333397664 96.49715333397664 40.0 56.0 46.4 8.763560920082657 76.8 0 0 0 0 0 0 0 0 1.0 58.0 40.0 56.0 40 0 0 0 0 0 0 2 80 2 112 -1 -1 1 20 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 0 local ip address 58891 foreign ip address 53 17 20849 2 2 82.0 114.0 41.0 41.0 41.0 0.0 57.0 57.0 57.0 0.0 9400.9305002638 191.85572449517963 6949.666666666666 12032.84564570382 20844.0 2.0 3.0 3.0 0.0 3.0 3.0 2.0 2.0 0.0 2.0 2.0 0 0 0 0 40 40 95.9278622475898 95.9278622475898 41.0 57.0 47.4 8.763560920082657 76.8 0 0 0 0 0 0 0 0 1.0 59.25 41.0 57.0 40 0 0 0 0 0 0 2 82 2 114 -1 -1 1 20 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 0" |
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example_title: "8. malicious then benign same entry" |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# bert-unformatted-network-data-test |
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This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0000 |
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# EXAMPLE FULL NAMES: |
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label_0 = malicious (UDP-lag DDoS), label_1 = benign |
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1. malicious from training dataset |
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2. benign from training dataset |
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3. malicious outside training dataset |
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4. malicious outside training dataset 2 |
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5. benign outside training dataset |
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6. benign outside training dataset 2 |
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7. benign then malicious same entry |
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8. malicious then benign same entry |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 0.0139 | 1.0 | 750 | 0.0000 | |
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| 0.0 | 2.0 | 1500 | 0.0000 | |
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| 0.0 | 3.0 | 2250 | 0.0000 | |
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| 0.0 | 4.0 | 3000 | 0.0000 | |
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| 0.0 | 5.0 | 3750 | 0.0000 | |
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### Framework versions |
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- Transformers 4.42.0.dev0 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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