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---
language:
- en
base_model:
- CrabInHoney/urlbert-tiny-base-v3
pipeline_tag: text-classification
tags:
- url
- cybersecurity
- urls
- links
- classification
- phishing-detection
- tiny
- phishing
- malware
- defacement
- transformers
- urlbert
- bert
- malicious
license: apache-2.0
new_version: CrabInHoney/urlbert-tiny-v4-malicious-url-classifier
---
# URLBERT-Tiny-v3 Malicious URL Classifier
This is a lightweight version of BERT, specifically fine-tuned for classifying URLs into four categories: benign, phishing, malware, and defacement.
## Model Details
- **Model size**: 3.69M parameters
- **Tensor type**: F32
- **Model weight size**: 14.8 MB
- **Base model**: [CrabInHoney/urlbert-tiny-base-v3](https://huggingface.co/CrabInHoney/urlbert-tiny-base-v3)
- **Dataset**: [Malicious URLs Dataset](https://www.kaggle.com/datasets/sid321axn/malicious-urls-dataset)
## Model Evaluation Results
The model was evaluated on a test set with the following classification metrics:
| Class | Precision | Recall | F1-Score |
|--------------|------------|------------|------------|
| Benign | 0.987695 | 0.993717 | 0.990697 |
| Defacement | 0.988510 | 0.998963 | 0.993709 |
| Malware | 0.988291 | 0.960332 | 0.974111 |
| Phishing | 0.958425 | 0.930826 | 0.944423 |
| **Accuracy** | 0.983738 | 0.983738 | 0.983738 |
| **Macro Avg**| 0.980730 | 0.970959 | 0.975735 |
| **Weighted Avg** | 0.983615 | 0.983738 | 0.983627 |
## Usage Example
Below is an example of how to use the model for URL classification using the Hugging Face `transformers` library:
```python
from transformers import BertTokenizerFast, BertForSequenceClassification, pipeline
import torch
# Определение устройства (GPU или CPU)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f"Используемое устройство: {device}")
# Загрузка модели и токенизатора
model_name = "CrabInHoney/urlbert-tiny-v3-malicious-url-classifier"
tokenizer = BertTokenizerFast.from_pretrained(model_name)
model = BertForSequenceClassification.from_pretrained(model_name)
model.to(device)
# Создание pipeline для классификации
classifier = pipeline(
"text-classification",
model=model,
tokenizer=tokenizer,
device=0 if torch.cuda.is_available() else -1,
return_all_scores=True
)
# Примеры URL для тестирования
test_urls = [
"wikiobits.com/Obits/TonyProudfoot",
"http://www.824555.com/app/member/SportOption.php?uid=guest&langx=gb",
]
# Маппинг меток на понятные названия классов
label_mapping = {
"LABEL_0": "benign",
"LABEL_1": "defacement",
"LABEL_2": "malware",
"LABEL_3": "phishing"
}
# Классификация URL
for url in test_urls:
results = classifier(url)
print(f"\nURL: {url}")
for result in results[0]:
label = result['label']
score = result['score']
friendly_label = label_mapping.get(label, label)
print(f"Класс: {friendly_label}, вероятность: {score:.4f}")
```
### Example Output:
```
URL: wikiobits.com/Obits/TonyProudfoot
Класс: benign, вероятность: 0.9953
Класс: defacement, вероятность: 0.0000
Класс: malware, вероятность: 0.0000
Класс: phishing, вероятность: 0.0046
URL: http://www.824555.com/app/member/SportOption.php?uid=guest&langx=gb
Класс: benign, вероятность: 0.0000
Класс: defacement, вероятность: 0.0001
Класс: malware, вероятность: 0.9998
Класс: phishing, вероятность: 0.0001
``` |