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Delete CIC-IDS2017.py

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  1. CIC-IDS2017.py +0 -131
CIC-IDS2017.py DELETED
@@ -1,131 +0,0 @@
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- import os
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- import datasets
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- import pandas as pd
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-
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-
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- _CITATION = """\
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- @article{maharajan2020attack,
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- title={Attack classification and intrusion detection in IoT network using machine learning techniques},
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- author={Maharajan, R and Raja, KS},
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- journal={Computers \& Electrical Engineering},
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- volume={87},
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- pages={106783},
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- year={2020},
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- publisher={Elsevier}
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- }"""
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-
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- _DESCRIPTION = """\
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- The CIC-IDS2017 dataset is an intrusion detection dataset that consists of network traffic data. \
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- It contains different network attacks and normal traffic. This dataset can be used for evaluating \
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- intrusion detection systems in IoT networks.
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- """
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-
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- _HOMEPAGE = "https://www.unb.ca/cic/datasets/ids-2017.html"
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-
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- _LICENSE = "Unknown"
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-
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- _FOLDERS = {
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- "folder_1": "Network-Flows",
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- "folder_2": "Payload-Bytes",
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- "folder_3": "Packet-Bytes",
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- "folder_4": "Packet-Fields",
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- }
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-
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-
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- class CICIDS2017(datasets.GeneratorBasedBuilder):
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- VERSION = datasets.Version("1.0.0")
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-
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- BUILDER_CONFIGS = [
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- datasets.BuilderConfig(name="folder_1", version=VERSION, description="Folder 1 of CIC-IDS2017 dataset"),
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- datasets.BuilderConfig(name="folder_2", version=VERSION, description="Folder 2 of CIC-IDS2017 dataset"),
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- datasets.BuilderConfig(name="folder_3", version=VERSION, description="Folder 3 of CIC-IDS2017 dataset"),
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- datasets.BuilderConfig(name="folder_4", version=VERSION, description="Folder 4 of CIC-IDS2017 dataset"),
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- ]
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-
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- DEFAULT_CONFIG_NAME = "folder_1"
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-
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- def _info(self):
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- if self.config.name == "folder_1":
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- features = datasets.Features(
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- {
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- "source_ip": datasets.Value("string"),
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- "destination_ip": datasets.Value("string"),
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- "timestamp": datasets.Value("string"),
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- "protocol": datasets.Value("string"),
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- "flow_duration": datasets.Value("float"),
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- # Add more features specific to folder_1 configuration
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- }
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- )
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- elif self.config.name == "folder_2":
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- features = datasets.Features(
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- {
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- "source_ip": datasets.Value("string"),
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- "destination_ip": datasets.Value("string"),
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- "timestamp": datasets.Value("string"),
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- "protocol": datasets.Value("string"),
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- "flow_duration": datasets.Value("float"),
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- # Add more features specific to folder_2 configuration
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- }
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- )
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- elif self.config.name == "folder_3":
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- features = datasets.Features(
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- {
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- "source_ip": datasets.Value("string"),
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- "destination_ip": datasets.Value("string"),
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- "timestamp": datasets.Value("string"),
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- "protocol": datasets.Value("string"),
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- "flow_duration": datasets.Value("float"),
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- # Add more features specific to folder_3 configuration
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- }
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- )
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- else: # folder_4
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- features = datasets.Features(
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- {
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- "source_ip": datasets.Value("string"),
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- "destination_ip": datasets.Value("string"),
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- "timestamp": datasets.Value("string"),
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- "protocol": datasets.Value("string"),
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- "flow_duration": datasets.Value("float"),
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- # Add more features specific to folder_4 configuration
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- }
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- )
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-
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- return datasets.DatasetInfo(
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- description=_DESCRIPTION,
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- features=features,
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- homepage=_HOMEPAGE,
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- license=_LICENSE,
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- citation=_CITATION,
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- )
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-
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- def _split_generators(self, dl_manager):
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- folder_path = _FOLDERS[self.config.name]
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- data_dir = dl_manager.download(folder_path)
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- csv_files = [
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- filename for filename in os.listdir(data_dir) if filename.endswith(".csv")
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- ]
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-
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- return [
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- datasets.SplitGenerator(
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- name=datasets.Split.TRAIN,
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- gen_kwargs={"data_dir": data_dir, "csv_files": csv_files},
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- )
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- ]
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-
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- def _generate_examples(self, data_dir, csv_files):
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- for csv_file in csv_files:
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- file_path = os.path.join(data_dir, csv_file)
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- df = pd.read_csv(file_path)
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- for idx, row in df.iterrows():
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- example = {
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- "source_ip": row["source_ip"],
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- "destination_ip": row["destination_ip"],
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- "timestamp": row["timestamp"],
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- "protocol": row["protocol"],
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- "flow_duration": row["flow_duration"],
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- # Add more feature values according to the dataset columns
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- }
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- yield idx, example
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-
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-
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- datasets.load_dataset("rdpahalavan/CIC-IDS2017")