metadata
license: mit
base_model: roberta-large
tags:
- generated_from_trainer
model-index:
- name: bert-unformatted-network-data-test
results: []
widget:
- 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
example_title: 1. malicious from training dataset
- 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
example_title: 2. benign from training dataset
- 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
example_title: 3. malicious outside training dataset
- 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
example_title: 4. malicious outside training dataset 2
- 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
example_title: 5. benign outside training dataset
- 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
example_title: 6. benign outside training dataset 2
- 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
example_title: 7. benign then malicious same entry
- 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
example_title: 8. malicious then benign same entry
bert-unformatted-network-data-test
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
EXAMPLE FULL NAMES:
label_0 = malicious (UDP-lag DDoS), label_1 = benign
- malicious from training dataset
- benign from training dataset
- malicious outside training dataset
- malicious outside training dataset 2
- benign outside training dataset
- benign outside training dataset 2
- benign then malicious same entry
- malicious then benign same entry
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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.0139 | 1.0 | 750 | 0.0000 |
| 0.0 | 2.0 | 1500 | 0.0000 |
| 0.0 | 3.0 | 2250 | 0.0000 |
| 0.0 | 4.0 | 3000 | 0.0000 |
| 0.0 | 5.0 | 3750 | 0.0000 |
Framework versions
- Transformers 4.42.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1