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import os |
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import datasets |
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import pandas as pd |
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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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_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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_HOMEPAGE = "https://www.unb.ca/cic/datasets/ids-2017.html" |
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_LICENSE = "Unknown" |
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_FOLDERS = { |
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"folder_1": "rdpahalavan/CIC-IDS2017/Network-Flows", |
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"folder_2": "rdpahalavan/CIC-IDS2017/Payload-Bytes", |
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"folder_3": "rdpahalavan/CIC-IDS2017/Packet-Bytes", |
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"folder_4": "rdpahalavan/CIC-IDS2017/Packet-Fields", |
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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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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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DEFAULT_CONFIG_NAME = "folder_1" |
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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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} |
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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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} |
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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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} |
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) |
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else: |
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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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} |
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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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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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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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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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} |
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yield idx, example |
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datasets.load_dataset("rdpahalavan/CIC-IDS2017") |