transformers tensorboard safetensors bert token-classification generated_from_trainer base_model:jackaduma/SecBERT base_model:finetune:jackaduma/SecBERT license:apache-2.0 autotrain_compatible endpoints_compatible region:us
---
base_model: jackaduma/SecBERT
license: apache-2.0
metrics:
- precision
- recall
- f1
- accuracy
tags:
- generated_from_trainer
model-index:
- name: Cyber-ThreaD/SecBERT-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/SecBERT-AttackER

This model is a fine-tuned version of [jackaduma/SecBERT](https://huggingface.co/jackaduma/SecBERT) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6932
- Precision: 0.3931
- Recall: 0.4987
- F1: 0.4397
- Accuracy: 0.7295

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.7927        | 0.4   | 500   | 1.5607          | 0.0956    | 0.0780 | 0.0859 | 0.6139   |
| 1.3551        | 0.81  | 1000  | 1.3530          | 0.2064    | 0.2381 | 0.2211 | 0.6495   |
| 1.0432        | 1.21  | 1500  | 1.3107          | 0.2269    | 0.3082 | 0.2614 | 0.6740   |
| 0.8468        | 1.61  | 2000  | 1.2497          | 0.2447    | 0.3373 | 0.2836 | 0.6767   |
| 0.7775        | 2.01  | 2500  | 1.2710          | 0.2895    | 0.3730 | 0.3260 | 0.6939   |
| 0.5374        | 2.42  | 3000  | 1.3020          | 0.3006    | 0.4048 | 0.3450 | 0.7044   |
| 0.5071        | 2.82  | 3500  | 1.2614          | 0.2959    | 0.4048 | 0.3419 | 0.7081   |
| 0.4237        | 3.22  | 4000  | 1.3251          | 0.3367    | 0.4405 | 0.3817 | 0.7166   |
| 0.3597        | 3.63  | 4500  | 1.3853          | 0.3423    | 0.4524 | 0.3897 | 0.7125   |
| 0.3632        | 4.03  | 5000  | 1.4156          | 0.3559    | 0.4524 | 0.3984 | 0.7127   |
| 0.2589        | 4.43  | 5500  | 1.4472          | 0.3504    | 0.4709 | 0.4018 | 0.7173   |
| 0.323         | 4.83  | 6000  | 1.3997          | 0.3452    | 0.4603 | 0.3946 | 0.7222   |
| 0.2167        | 5.24  | 6500  | 1.5194          | 0.3467    | 0.4590 | 0.3950 | 0.7233   |
| 0.2363        | 5.64  | 7000  | 1.5585          | 0.3507    | 0.4722 | 0.4025 | 0.7222   |
| 0.2721        | 6.04  | 7500  | 1.5420          | 0.3715    | 0.4854 | 0.4209 | 0.7210   |
| 0.2073        | 6.45  | 8000  | 1.5878          | 0.3536    | 0.4854 | 0.4091 | 0.7147   |
| 0.2021        | 6.85  | 8500  | 1.6637          | 0.3722    | 0.4854 | 0.4214 | 0.7197   |
| 0.1648        | 7.25  | 9000  | 1.6724          | 0.3795    | 0.4788 | 0.4234 | 0.7255   |
| 0.1927        | 7.66  | 9500  | 1.6891          | 0.3801    | 0.4947 | 0.4299 | 0.7245   |
| 0.1958        | 8.06  | 10000 | 1.6774          | 0.3937    | 0.4974 | 0.4395 | 0.7281   |
| 0.1508        | 8.46  | 10500 | 1.7379          | 0.3815    | 0.4854 | 0.4272 | 0.7259   |
| 0.184         | 8.86  | 11000 | 1.7001          | 0.3863    | 0.5013 | 0.4364 | 0.7277   |
| 0.1696        | 9.27  | 11500 | 1.6932          | 0.3931    | 0.4987 | 0.4397 | 0.7295   |
| 0.1425        | 9.67  | 12000 | 1.7137          | 0.3824    | 0.5013 | 0.4339 | 0.7276   |


### 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}
}

```
