Create nbaiot.py

nbaiot.py

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+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""N-BaIoT dataset loader."""
+
+
+import os
+import rarfile
+import numpy as np
+import pandas as pd
+import datasets
+
+
+_CITATION = """\
+@article{DBLP:journals/corr/abs-1805-03409,
+  author       = {Yair Meidan and
+                  Michael Bohadana and
+                  Yael Mathov and
+                  Yisroel Mirsky and
+                  Dominik Breitenbacher and
+                  Asaf Shabtai and
+                  Yuval Elovici},
+  title        = {N-BaIoT: Network-based Detection of IoT Botnet Attacks Using Deep
+                  Autoencoders},
+  journal      = {CoRR},
+  volume       = {abs/1805.03409},
+  year         = {2018},
+  url          = {http://arxiv.org/abs/1805.03409},
+  eprinttype    = {arXiv},
+  eprint       = {1805.03409},
+  timestamp    = {Mon, 13 Aug 2018 16:49:04 +0200},
+  biburl       = {https://dblp.org/rec/journals/corr/abs-1805-03409.bib},
+  bibsource    = {dblp computer science bibliography, https://dblp.org}
+}
+"""
+_DESCRIPTION = """\
+An intrusion detection dataset that focuses on IoT botnet attacks.
+"""
+_HOMEPAGE = "https://archive.ics.uci.edu/dataset/442/detection+of+iot+botnet+attacks+n+baiot"
+_LICENSE = "Creative Commons Attribution 4.0 International (CC BY 4.0) license"
+_URL = 'https://archive.ics.uci.edu/static/public/442/detection+of+iot+botnet+attacks+n+baiot.zip'
+_ATTACK_NAMES = ['benign_traffic', 'combo', 'junk', 'mirai-ack', 'mirai-scan', 'mirai-syn', 'mirai-udp', 'mirai-udpplain', 'scan', 'tcp', 'udp']
+_DEVICE_NAMES = [
+    "Danmini_Doorbell", "Ecobee_Thermostat", "Ennio_Doorbell", "Philips_B120N10_Baby_Monitor", "Provision_PT_737E_Security_Camera",
+    "Provision_PT_838_Security_Camera", "Samsung_SNH_1011_N_Webcam", "SimpleHome_XCS7_1002_WHT_Security_Camera",
+    "SimpleHome_XCS7_1003_WHT_Security_Camera"
+]
+
+
+class NBAIOTDataset(datasets.GeneratorBasedBuilder):
+    """N-BaIoT intrusion detection."""
+
+    VERSION = datasets.Version("1.1.0")
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            features=datasets.Features({
+                "features": datasets.Sequence(feature=datasets.Value("float32"), length=115),
+                "attack": datasets.ClassLabel(len(_ATTACK_NAMES), names=_ATTACK_NAMES),
+                "device": datasets.ClassLabel(len(_DEVICE_NAMES), names=_DEVICE_NAMES),
+            }),
+            # Homepage of the dataset for documentation
+            homepage=_HOMEPAGE,
+            # License for the dataset if available
+            license=_LICENSE,
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        data_dir = dl_manager.download_and_extract(_URL)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": data_dir,
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": data_dir,
+                    "split": "test"
+                },
+            ),
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, filepath, split):
+        for device in _DEVICE_NAMES:
+            # First load in the benign traffic
+            all_data = pd.read_csv(f"{filepath}/{device}/benign_traffic.csv")
+            all_data['attack'] = "benign_traffic"
+            # Then the standard attacks
+            attacks_rar = rarfile.RarFile(f"{filepath}/{device}/gafgyt_attacks.rar")
+            for fileinfo in attacks_rar.infolist():
+                with attacks_rar.open(fileinfo.filename) as f:
+                    df = pd.read_csv(f)
+                df['attack'] = f.name.replace(".csv", "")
+                all_data = pd.concat((all_data, df))
+            # And, if present, the Mirai attacks
+            if device not in ["Ennio_Doorbell", "Samsung_SNH_1011_N_Webcam"]:
+                mirai_rar = rarfile.RarFile(f"{filepath}/{device}/mirai_attacks.rar")
+                for fileinfo in mirai_rar.infolist():
+                    with mirai_rar.open(fileinfo.filename) as f:
+                        df = pd.read_csv(f)
+                    df['attack'] = "mirai-" + fileinfo.filename.replace(".csv", "")
+                    all_data = pd.concat((all_data, df))
+            # Create the train-test split
+            rng = np.random.default_rng(round(np.pi**(np.pi * 100)))
+            train = rng.uniform(size=len(all_data)) < 0.85
+            all_data = all_data.iloc[np.where(train if split == "train" else ~train)]
+            attacks = all_data['attack'].to_list()
+            all_data = all_data.drop(columns="attack")
+            # Finally yield the data
+            for (key, row), attack in zip(all_data.iterrows(), attacks):
+                yield key, {
+                    "features": row.to_numpy(),
+                    "attack": attack,
+                    "device": device,
+                }