Delete CIC-IDS2017.py

CIC-IDS2017.py

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