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training
This model is a fine-tuned version of roberta-base on the cynthiachan/FeedRef_10pct dataset. It achieves the following results on the evaluation set:
- Loss: 0.1033
- Attackid Precision: 1.0
- Attackid Recall: 1.0
- Attackid F1: 1.0
- Attackid Number: 6
- Cve Precision: 1.0
- Cve Recall: 1.0
- Cve F1: 1.0
- Cve Number: 11
- Defenderthreat Precision: 0.0
- Defenderthreat Recall: 0.0
- Defenderthreat F1: 0.0
- Defenderthreat Number: 2
- Domain Precision: 0.8636
- Domain Recall: 0.8261
- Domain F1: 0.8444
- Domain Number: 23
- Email Precision: 1.0
- Email Recall: 1.0
- Email F1: 1.0
- Email Number: 3
- Filepath Precision: 0.8108
- Filepath Recall: 0.9091
- Filepath F1: 0.8571
- Filepath Number: 165
- Hostname Precision: 0.9231
- Hostname Recall: 1.0
- Hostname F1: 0.9600
- Hostname Number: 12
- Ipv4 Precision: 0.9167
- Ipv4 Recall: 0.9167
- Ipv4 F1: 0.9167
- Ipv4 Number: 12
- Md5 Precision: 0.875
- Md5 Recall: 0.9423
- Md5 F1: 0.9074
- Md5 Number: 52
- Sha1 Precision: 0.75
- Sha1 Recall: 0.8571
- Sha1 F1: 0.8000
- Sha1 Number: 7
- Sha256 Precision: 0.8
- Sha256 Recall: 1.0
- Sha256 F1: 0.8889
- Sha256 Number: 44
- Uri Precision: 0.0
- Uri Recall: 0.0
- Uri F1: 0.0
- Uri Number: 1
- Overall Precision: 0.8383
- Overall Recall: 0.9201
- Overall F1: 0.8773
- Overall Accuracy: 0.9816
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: 5e-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: 3.0
Training results
Training Loss | Epoch | Step | Validation Loss | Attackid Precision | Attackid Recall | Attackid F1 | Attackid Number | Cve Precision | Cve Recall | Cve F1 | Cve Number | Defenderthreat Precision | Defenderthreat Recall | Defenderthreat F1 | Defenderthreat Number | Domain Precision | Domain Recall | Domain F1 | Domain Number | Email Precision | Email Recall | Email F1 | Email Number | Filepath Precision | Filepath Recall | Filepath F1 | Filepath Number | Hostname Precision | Hostname Recall | Hostname F1 | Hostname Number | Ipv4 Precision | Ipv4 Recall | Ipv4 F1 | Ipv4 Number | Md5 Precision | Md5 Recall | Md5 F1 | Md5 Number | Sha1 Precision | Sha1 Recall | Sha1 F1 | Sha1 Number | Sha256 Precision | Sha256 Recall | Sha256 F1 | Sha256 Number | Uri Precision | Uri Recall | Uri F1 | Uri Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0.4353 | 0.37 | 500 | 0.3525 | 0.0 | 0.0 | 0.0 | 6 | 0.0 | 0.0 | 0.0 | 11 | 0.0 | 0.0 | 0.0 | 2 | 0.0 | 0.0 | 0.0 | 23 | 0.0 | 0.0 | 0.0 | 3 | 0.3984 | 0.6182 | 0.4846 | 165 | 0.0714 | 0.3333 | 0.1176 | 12 | 0.0 | 0.0 | 0.0 | 12 | 0.8936 | 0.8077 | 0.8485 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.4937 | 0.8864 | 0.6341 | 44 | 0.0 | 0.0 | 0.0 | 1 | 0.4156 | 0.5533 | 0.4746 | 0.9459 |
0.2089 | 0.75 | 1000 | 0.1812 | 0.0 | 0.0 | 0.0 | 6 | 0.9 | 0.8182 | 0.8571 | 11 | 0.0 | 0.0 | 0.0 | 2 | 0.15 | 0.2609 | 0.1905 | 23 | 0.0 | 0.0 | 0.0 | 3 | 0.6432 | 0.7758 | 0.7033 | 165 | 0.0 | 0.0 | 0.0 | 12 | 0.6471 | 0.9167 | 0.7586 | 12 | 0.7143 | 0.8654 | 0.7826 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.5286 | 0.8409 | 0.6491 | 44 | 0.0 | 0.0 | 0.0 | 1 | 0.5315 | 0.6982 | 0.6036 | 0.9626 |
0.1453 | 1.12 | 1500 | 0.1374 | 0.75 | 0.5 | 0.6 | 6 | 0.9167 | 1.0 | 0.9565 | 11 | 0.0 | 0.0 | 0.0 | 2 | 0.5135 | 0.8261 | 0.6333 | 23 | 0.0 | 0.0 | 0.0 | 3 | 0.6863 | 0.8485 | 0.7588 | 165 | 0.7 | 0.5833 | 0.6364 | 12 | 0.6667 | 0.6667 | 0.6667 | 12 | 0.8167 | 0.9423 | 0.8750 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.8333 | 0.9091 | 0.8696 | 44 | 0.0 | 0.0 | 0.0 | 1 | 0.7048 | 0.8195 | 0.7579 | 0.9745 |
0.1277 | 1.5 | 2000 | 0.1400 | 1.0 | 1.0 | 1.0 | 6 | 1.0 | 1.0 | 1.0 | 11 | 0.0 | 0.0 | 0.0 | 2 | 0.7273 | 0.6957 | 0.7111 | 23 | 0.2 | 0.3333 | 0.25 | 3 | 0.7181 | 0.8182 | 0.7649 | 165 | 0.9167 | 0.9167 | 0.9167 | 12 | 0.7857 | 0.9167 | 0.8462 | 12 | 0.8167 | 0.9423 | 0.8750 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.8302 | 1.0 | 0.9072 | 44 | 0.0 | 0.0 | 0.0 | 1 | 0.7634 | 0.8402 | 0.8000 | 0.9735 |
0.1074 | 1.87 | 2500 | 0.1101 | 1.0 | 1.0 | 1.0 | 6 | 1.0 | 1.0 | 1.0 | 11 | 0.0 | 0.0 | 0.0 | 2 | 0.72 | 0.7826 | 0.7500 | 23 | 0.2857 | 0.6667 | 0.4 | 3 | 0.7554 | 0.8424 | 0.7966 | 165 | 0.8571 | 1.0 | 0.9231 | 12 | 0.8182 | 0.75 | 0.7826 | 12 | 0.9259 | 0.9615 | 0.9434 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.6833 | 0.9318 | 0.7885 | 44 | 0.0 | 0.0 | 0.0 | 1 | 0.7660 | 0.8521 | 0.8067 | 0.9762 |
0.0758 | 2.25 | 3000 | 0.1161 | 1.0 | 1.0 | 1.0 | 6 | 1.0 | 1.0 | 1.0 | 11 | 0.0 | 0.0 | 0.0 | 2 | 0.9091 | 0.8696 | 0.8889 | 23 | 0.5 | 0.6667 | 0.5714 | 3 | 0.8251 | 0.9152 | 0.8678 | 165 | 1.0 | 1.0 | 1.0 | 12 | 1.0 | 0.6667 | 0.8 | 12 | 0.9259 | 0.9615 | 0.9434 | 52 | 1.0 | 0.5714 | 0.7273 | 7 | 0.8958 | 0.9773 | 0.9348 | 44 | 0.0 | 0.0 | 0.0 | 1 | 0.8722 | 0.9083 | 0.8899 | 0.9814 |
0.064 | 2.62 | 3500 | 0.1275 | 1.0 | 1.0 | 1.0 | 6 | 0.8333 | 0.9091 | 0.8696 | 11 | 0.0 | 0.0 | 0.0 | 2 | 0.8947 | 0.7391 | 0.8095 | 23 | 1.0 | 1.0 | 1.0 | 3 | 0.8418 | 0.9030 | 0.8713 | 165 | 0.8571 | 1.0 | 0.9231 | 12 | 1.0 | 0.75 | 0.8571 | 12 | 0.9245 | 0.9423 | 0.9333 | 52 | 0.6667 | 0.5714 | 0.6154 | 7 | 0.8113 | 0.9773 | 0.8866 | 44 | 0.0 | 0.0 | 0.0 | 1 | 0.8580 | 0.8935 | 0.8754 | 0.9793 |
0.0522 | 3.0 | 4000 | 0.1033 | 1.0 | 1.0 | 1.0 | 6 | 1.0 | 1.0 | 1.0 | 11 | 0.0 | 0.0 | 0.0 | 2 | 0.8636 | 0.8261 | 0.8444 | 23 | 1.0 | 1.0 | 1.0 | 3 | 0.8108 | 0.9091 | 0.8571 | 165 | 0.9231 | 1.0 | 0.9600 | 12 | 0.9167 | 0.9167 | 0.9167 | 12 | 0.875 | 0.9423 | 0.9074 | 52 | 0.75 | 0.8571 | 0.8000 | 7 | 0.8 | 1.0 | 0.8889 | 44 | 0.0 | 0.0 | 0.0 | 1 | 0.8383 | 0.9201 | 0.8773 | 0.9816 |
Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu102
- Datasets 2.4.0
- Tokenizers 0.12.1