Instructions to use JabaleNurAdnan/CS_Checkpoints_Dataset with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use JabaleNurAdnan/CS_Checkpoints_Dataset with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://JabaleNurAdnan/CS_Checkpoints_Dataset") - Notebooks
- Google Colab
- Kaggle
File size: 926 Bytes
0b74c98 | 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 | {
"name": "CICIDS2017",
"n_train": 2264594,
"n_test": 566149,
"n_features": 30,
"features": [
"Packet Length Variance",
"Average Packet Size",
"Packet Length Std",
"Packet Length Mean",
"Total Length of Bwd Packets",
"Subflow Bwd Bytes",
"Subflow Fwd Bytes",
"Total Length of Fwd Packets",
"Bwd Packet Length Mean",
"Avg Bwd Segment Size",
"Init_Win_bytes_forward",
"Destination Port",
"Bwd Packet Length Max",
"Max Packet Length",
"Fwd Packet Length Max",
"Init_Win_bytes_backward",
"Flow IAT Max",
"Flow Bytes/s",
"Flow Duration",
"Bwd Packets/s",
"Bwd Packet Length Std",
"Avg Fwd Segment Size",
"Fwd Packet Length Mean",
"Bwd Header Length",
"Fwd Packets/s",
"Fwd IAT Max",
"Fwd Header Length",
"Fwd Header Length.1",
"Flow Packets/s",
"Fwd IAT Total"
],
"n_clients": 30,
"alpha": 0.5
} |