It's very important to note that this model is not production-ready.
The classification task for v1 is split into two stages:
- URL features model
- 96.5%+ accurate on training and validation data
- 2,436,727 rows of labelled URLs
- evaluation from v2: slightly overfitted, by perhaps around 0.8%
- Website features model
- 98.4% accurate on training data, and 98.9% accurate on validation data
- 911,180 rows of 42 features
- evaluation from v2: slightly biased towards the URL feature (bert_confidence) more than the other columns
Training
I applied cross-validation with cv=5
to the training dataset to search for the best hyperparameters.
Here's the dict passed to sklearn
's GridSearchCV
function:
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 on 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")
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