File size: 3,276 Bytes
7eb0432 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 |
---
language:
- en
base_model:
- CrabInHoney/urlbert-tiny-base-v4
pipeline_tag: text-classification
tags:
- url
- cybersecurity
- urls
- links
- classification
- phishing-detection
- tiny
- phishing
- malware
- defacement
- transformers
- urlbert
- bert
- malicious
license: apache-2.0
---
# URLBERT-Tiny-v4 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-v4](https://huggingface.co/CrabInHoney/urlbert-tiny-base-v4)
- **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:
| Metric | Model V3 | Model V4 (this model) |
|--------|----------|----------|
| **Overall Accuracy** | 0.9837 | **0.9922** |
| **F1-score (Benign)** | 0.9907 | **0.9955** |
| **F1-score (Defacement)** | 0.9937 | **0.9984** |
| **F1-score (Malware)** | 0.9741 | **0.9845** |
| **F1-score (Phishing)** | 0.9444 | **0.9734** |
| **Weighted Average F1-score** | 0.9836 | **0.9922** |
## 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-v4-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"{friendly_label}, %: {score:.4f}")
```
### Example Output:
```
URL: wikiobits.com/Obits/TonyProudfoot
benign, %: 0.9996
defacement, %: 0.0000
malware, %: 0.0000
phishing, %: 0.0003
URL: http://www.824555.com/app/member/SportOption.php?uid=guest&langx=gb
benign, %: 0.0000
defacement, %: 0.0001
malware, %: 0.9998
phishing, %: 0.0001
``` |