metadata

license: bigscience-openrail-m
base_model: ehsanaghaei/SecureBERT
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: Cyber-ThreaD/SecureBERT-DNRTI
    results: []

Cyber-ThreaD/SecureBERT-DNRTI

This model is a fine-tuned version of ehsanaghaei/SecureBERT on the DNRTI dataset. It achieves the following results on the evaluation set:

Loss: 0.2427
Precision: 0.7694
Recall: 0.7854
F1: 0.7773
Accuracy: 0.9382

It achieves the following results on the prediction set:

Precision: 0.8346
Recall: 0.8403
F1: 0.8374
Accuracy: 0.9554

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: 10.0

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.7821	0.76	500	0.4215	0.5219	0.5919	0.5547	0.8745
0.3559	1.52	1000	0.3152	0.6272	0.6587	0.6426	0.9008
0.2807	2.28	1500	0.2952	0.6445	0.7232	0.6816	0.9084
0.2272	3.04	2000	0.2793	0.6682	0.7513	0.7073	0.9161
0.1837	3.81	2500	0.2489	0.7151	0.7526	0.7334	0.9258
0.1497	4.57	3000	0.2511	0.7254	0.7826	0.7529	0.9286
0.1371	5.33	3500	0.2496	0.7425	0.7757	0.7587	0.9331
0.1135	6.09	4000	0.2554	0.7289	0.8075	0.7662	0.9325
0.1018	6.85	4500	0.2427	0.7694	0.7854	0.7773	0.9382
0.0899	7.61	5000	0.2516	0.7583	0.8167	0.7864	0.9378
0.0809	8.37	5500	0.2459	0.7717	0.8176	0.7940	0.9406
0.0763	9.13	6000	0.2553	0.7518	0.8217	0.7852	0.9392
0.0687	9.89	6500	0.2534	0.7621	0.8204	0.7902	0.9407

Framework versions

Transformers 4.36.0.dev0
Pytorch 2.1.0+cu118
Datasets 2.14.6
Tokenizers 0.14.1

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}
}