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---
license: bigscience-openrail-m
base_model: ehsanaghaei/SecureBERT
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Cyber-ThreaD/SecureBERT-AttackER
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Cyber-ThreaD/SecureBERT-AttackER
This model is a fine-tuned version of [ehsanaghaei/SecureBERT](https://huggingface.co/ehsanaghaei/SecureBERT) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4668
- Precision: 0.4762
- Recall: 0.5291
- F1: 0.5013
- Accuracy: 0.7376
## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.9177 | 0.4 | 500 | 1.6839 | 0.06 | 0.0278 | 0.0380 | 0.6004 |
| 1.4976 | 0.81 | 1000 | 1.4936 | 0.2281 | 0.2659 | 0.2456 | 0.6313 |
| 1.2309 | 1.21 | 1500 | 1.2915 | 0.2650 | 0.3148 | 0.2878 | 0.6657 |
| 1.0546 | 1.61 | 2000 | 1.2454 | 0.2950 | 0.3796 | 0.3320 | 0.6804 |
| 0.9405 | 2.01 | 2500 | 1.2377 | 0.3613 | 0.3532 | 0.3572 | 0.6916 |
| 0.7501 | 2.42 | 3000 | 1.1723 | 0.3607 | 0.4180 | 0.3873 | 0.7171 |
| 0.7133 | 2.82 | 3500 | 1.1584 | 0.3632 | 0.4444 | 0.3998 | 0.7160 |
| 0.5896 | 3.22 | 4000 | 1.2288 | 0.4103 | 0.4444 | 0.4267 | 0.7306 |
| 0.5353 | 3.63 | 4500 | 1.2319 | 0.3978 | 0.4815 | 0.4357 | 0.7254 |
| 0.5432 | 4.03 | 5000 | 1.2173 | 0.4269 | 0.4868 | 0.4549 | 0.7306 |
| 0.4062 | 4.43 | 5500 | 1.2832 | 0.4398 | 0.5026 | 0.4691 | 0.7272 |
| 0.4485 | 4.83 | 6000 | 1.2196 | 0.4212 | 0.5093 | 0.4611 | 0.7412 |
| 0.3614 | 5.24 | 6500 | 1.3155 | 0.4325 | 0.4960 | 0.4621 | 0.7325 |
| 0.3308 | 5.64 | 7000 | 1.3501 | 0.4184 | 0.5119 | 0.4604 | 0.7354 |
| 0.3645 | 6.04 | 7500 | 1.3391 | 0.4359 | 0.5172 | 0.4731 | 0.7366 |
| 0.2982 | 6.45 | 8000 | 1.3889 | 0.4093 | 0.5225 | 0.4590 | 0.7315 |
| 0.2845 | 6.85 | 8500 | 1.4109 | 0.4452 | 0.5159 | 0.4779 | 0.7377 |
| 0.2482 | 7.25 | 9000 | 1.4668 | 0.4762 | 0.5291 | 0.5013 | 0.7376 |
| 0.2636 | 7.66 | 9500 | 1.4925 | 0.4540 | 0.5357 | 0.4915 | 0.7341 |
| 0.2605 | 8.06 | 10000 | 1.4916 | 0.4586 | 0.5344 | 0.4936 | 0.7405 |
| 0.1989 | 8.46 | 10500 | 1.5096 | 0.4661 | 0.5370 | 0.4991 | 0.7387 |
| 0.2415 | 8.86 | 11000 | 1.4698 | 0.4603 | 0.5450 | 0.4991 | 0.7443 |
| 0.2488 | 9.27 | 11500 | 1.4736 | 0.4578 | 0.5304 | 0.4914 | 0.7455 |
| 0.2129 | 9.67 | 12000 | 1.5067 | 0.4640 | 0.5450 | 0.5012 | 0.7439 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
### Citing & Authors
If you use the model kindly cite the following work
```
@inproceedings{deka2024attacker,
title={AttackER: Towards Enhancing Cyber-Attack Attribution with a Named Entity Recognition Dataset},
author={Deka, Pritam and Rajapaksha, Sampath and Rani, Ruby and Almutairi, Amirah and Karafili, Erisa},
booktitle={International Conference on Web Information Systems Engineering},
pages={255--270},
year={2024},
organization={Springer}
}
``` |