Text Classification
Scikit-learn
Joblib
Keras
English
cybersecurity
http-attack-detection
intrusion-detection
web-security
tfidf
xgboost
lightgbm
Instructions to use cycloevan/http-attack-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use cycloevan/http-attack-classification with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("cycloevan/http-attack-classification", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html - Keras
How to use cycloevan/http-attack-classification with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://cycloevan/http-attack-classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1466e90dc8044db008468805a4bd76b284de90518b151a4513eda76e8737d7a3
- Size of remote file:
- 1.47 GB
- SHA256:
- cf01be06c904a1a89aafe54e1e52db98cb3932c6901dc4c518db0f9f724060cd
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