File size: 5,986 Bytes
18bcc26 ccf4717 18bcc26 ccf4717 18bcc26 ccf4717 18bcc26 ccf4717 18bcc26 ccf4717 18bcc26 ccf4717 18bcc26 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 |
# 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").values
attacks = np.repeat("benign_traffic", len(all_data))
# 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:
data = pd.read_csv(f).values
attacks = np.concatenate((attacks, np.repeat(f.name.replace(".csv", ""), len(data))))
all_data = np.concatenate((all_data, data))
# 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:
data = pd.read_csv(f).values
attacks = np.concatenate((attacks, np.repeat("mirai-" + f.name.replace(".csv", ""), len(data))))
all_data = np.concatenate((all_data, data))
# 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[train if split == "train" else ~train]
# Finally yield the data
for key, (data, attack) in enumerate(zip(all_data, attacks)):
yield key, {
"features": data,
"attack": attack,
"device": device,
} |