Create CIC-IDS2017.py

CIC-IDS2017.py

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+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": "rdpahalavan/CIC-IDS2017/Network-Flows",
+    "folder_2": "rdpahalavan/CIC-IDS2017/Payload-Bytes",
+    "folder_3": "rdpahalavan/CIC-IDS2017/Packet-Bytes",
+    "folder_4": "rdpahalavan/CIC-IDS2017/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")