metadata
license: cc-by-nc-4.0
datasets:
- FredZhang7/malicious-website-features-2.4M
wget:
- text: https://chat.openai.com/
- text: https://huggingface.co/FredZhang7/aivance-safesearch-v3
metrics:
- accuracy
language:
- af
- en
- et
- sw
- sv
- sq
- de
- ca
- hu
- da
- tl
- so
- fi
- fr
- cs
- hr
- cy
- es
- sl
- tr
- pl
- pt
- nl
- id
- sk
- lt
- 'no'
- lv
- vi
- it
- ro
- ru
- mk
- bg
- th
- ja
- ko
- multilingual
The classification task is split into two stages:
- URL features model
- 96.5%+ accurate on training and validation data
- 2,436,727 rows of labelled URLs
- Website features model
- 98.4% accurate on training data, and 98.9% accurate on validation data
- 911,180 rows of 42 features
Training Features
I applied cross-validation with cv=5 to the training dataset to search for the best hyperparameters.
Here's the dict passed to GridSearchCV:
params = {
'objective': 'binary',
'metric': 'binary_logloss',
'boosting_type': ['gbdt', 'dart'],
'num_leaves': [15, 23, 31, 63],
'learning_rate': [0.001, 0.002, 0.01, 0.02],
'feature_fraction': [0.5, 0.6, 0.7, 0.9],
'early_stopping_rounds': [10, 20],
'num_boost_round': [500, 750, 800, 900, 1000, 1250, 2000]
}
To reproduce the 98.4% accurate model, you can follow the data analysis in the dataset page to filter out the unimportant features. Then train a LightGBM model using the most suited hyperparamters for this task:
params = {
'objective': 'binary',
'metric': 'binary_logloss',
'boosting_type': 'gbdt',
'num_leaves': 31,
'learning_rate': 0.01,
'feature_fraction': 0.6,
'early_stopping_rounds': 10,
'num_boost_round': 800
}
URL Features
from transformers import AutoModelForSequenceClassification, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("FredZhang7/malware-phisher")
model = AutoModelForSequenceClassification.from_pretrained("FredZhang7/malware-phisher")
Website Features
pip install lightgbm
import lightgbm as lgb
lgb.Booster(model_file="phishing_model_combined_0.984_train.txt")