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--- |
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license: cc-by-nc-4.0 |
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datasets: |
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- FredZhang7/malicious-website-features-2.4M |
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wget: |
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- text: https://chat.openai.com/ |
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- text: https://huggingface.co/FredZhang7/aivance-safesearch-v3 |
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metrics: |
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- accuracy |
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language: |
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- af |
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- en |
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- et |
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- sw |
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- sv |
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- sq |
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- de |
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- ca |
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- hu |
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- da |
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- tl |
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- so |
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- fi |
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- fr |
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- cs |
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- hr |
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- cy |
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- es |
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- sl |
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- tr |
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- pl |
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- pt |
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- nl |
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- id |
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- sk |
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- lt |
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- no |
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- lv |
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- vi |
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- it |
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- ro |
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- ru |
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- mk |
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- bg |
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- th |
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- ja |
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- ko |
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- multilingual |
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--- |
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The classification task is split into two stages: |
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1. URL features model |
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- 96.5%+ accuracy on training and validation data |
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- 2,436,727 rows of labelled URLs |
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2. Website features model |
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- 98.2% on training data, 98.7% accuracy on validation |
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- 911,180 rows of 11 features |
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## Training Features |
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I applied cross-validation with `cv=5` to the training dataset to search for the best hyperparameters. |
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Here's the dict passed to `GridSearchCV`: |
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```python |
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params = { |
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'objective': 'binary', |
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'metric': 'binary_logloss', |
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'boosting_type': ['gbdt', 'dart'], |
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'num_leaves': [15, 23, 31, 63], |
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'learning_rate': [0.001, 0.002, 0.01, 0.02], |
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'feature_fraction': [0.5, 0.6, 0.7, 0.9], |
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'early_stopping_rounds': [10, 20], |
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'num_boost_round': [500, 750, 800, 900, 1000, 1250, 2000] |
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} |
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``` |
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## URL Features |
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```python |
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from transformers import AutoModelForSequenceClassification, AutoTokenizer |
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tokenizer = AutoTokenizer.from_pretrained("FredZhang7/malware-phisher") |
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model = AutoModelForSequenceClassification.from_pretrained("FredZhang7/malware-phisher") |
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``` |
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## Website Features |
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```bash |
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pip install lightgbm |
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``` |
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```python |
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import lightgbm as lgb |
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lgb.Booster(model_file="phishing_model_100.0%_train+val.txt") |
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``` |
