kddcup / kddcup.py
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"""Kddcup Dataset"""
from typing import List
from functools import partial
import datasets
import pandas
VERSION = datasets.Version("1.0.0")
_ENCODING_DICS = {
"class": {value: i
for i, value in enumerate(["normal.",
"buffer_overflow.",
"loadmodule.",
"perl.",
"neptune.",
"smurf.",
"guess_passwd.",
"pod.",
"teardrop.",
"portsweep.",
"ipsweep.",
"land.",
"ftp_write.",
"back.",
"imap.",
"satan.",
"phf.",
"nmap.",
"multihop.",
"warezmaster.",
"warezclient.",
"spy.",
"rootkit."])
}
}
_BASE_FEATURE_NAMES = [
"duration",
"protocol_type",
"service",
"flag",
"src_bytes",
"dst_bytes",
"land",
"wrong_fragment",
"urgent",
"hot",
"num_failed_logins",
"logged_in",
"num_compromised",
"root_shell",
"su_attempted",
"num_root",
"num_file_creations",
"num_shells",
"num_access_files",
"num_outbound_cmds",
"is_host_login",
"is_guest_login",
"count",
"srv_count",
"serror_rate",
"srv_serror_rate",
"rerror_rate",
"srv_rerror_rate",
"same_srv_rate",
"diff_srv_rate",
"srv_diff_host_rate",
"dst_host_count",
"dst_host_srv_count",
"dst_host_same_srv_rate",
"dst_host_diff_srv_rate",
"dst_host_same_src_port_rate",
"dst_host_srv_diff_host_rate",
"dst_host_serror_rate",
"dst_host_srv_serror_rate",
"dst_host_rerror_rate",
"dst_host_srv_rerror_rate",
"class",
]
DESCRIPTION = "Kddcup dataset."
_HOMEPAGE = ""
_URLS = ("")
_CITATION = """"""
# Dataset info
urls_per_split = {
"train": "https://huggingface.co/datasets/mstz/kddcup/resolve/main/kddcup.data"
}
features_types_per_config = {
"kddcup": {
"duration": datasets.Value("float64"),
"protocol_type": datasets.Value("string"),
"service": datasets.Value("string"),
"flag": datasets.Value("string"),
"src_bytes": datasets.Value("int64"),
"dst_bytes": datasets.Value("int64"),
"land": datasets.Value("int64"),
"wrong_fragment": datasets.Value("int64"),
"urgent": datasets.Value("int64"),
"hot": datasets.Value("int64"),
"num_failed_logins": datasets.Value("int64"),
"logged_in": datasets.Value("int64"),
"num_compromised": datasets.Value("int64"),
"root_shell": datasets.Value("int64"),
"su_attempted": datasets.Value("int64"),
"num_root": datasets.Value("int64"),
"num_file_creations": datasets.Value("int64"),
"num_shells": datasets.Value("int64"),
"num_access_files": datasets.Value("int64"),
"num_outbound_cmds": datasets.Value("int64"),
"is_host_login": datasets.Value("int64"),
"is_guest_login": datasets.Value("int64"),
"count": datasets.Value("int64"),
"srv_count": datasets.Value("int64"),
"serror_rate": datasets.Value("float64"),
"srv_serror_rate": datasets.Value("float64"),
"rerror_rate": datasets.Value("float64"),
"srv_rerror_rate": datasets.Value("float64"),
"same_srv_rate": datasets.Value("float64"),
"diff_srv_rate": datasets.Value("float64"),
"srv_diff_host_rate": datasets.Value("float64"),
"dst_host_count": datasets.Value("int64"),
"dst_host_srv_count": datasets.Value("int64"),
"dst_host_same_srv_rate": datasets.Value("float64"),
"dst_host_diff_srv_rate": datasets.Value("float64"),
"dst_host_same_src_port_rate": datasets.Value("float64"),
"dst_host_srv_diff_host_rate": datasets.Value("float64"),
"dst_host_serror_rate": datasets.Value("float64"),
"dst_host_srv_serror_rate": datasets.Value("float64"),
"dst_host_rerror_rate": datasets.Value("float64"),
"dst_host_srv_rerror_rate": datasets.Value("float64"),
"class": datasets.ClassLabel(num_classes=23,
names=("normal.", "buffer_overflow.", "loadmodule.", "perl.", "neptune.",
"smurf.", "guess_passwd.", "pod.", "teardrop.", "portsweep.",
"ipsweep.", "land.", "ftp_write.", "back.", "imap.", "satan.",
"phf.", "nmap.", "multihop.", "warezmaster.", "warezclient.",
"spy.", "rootkit.")),
}
}
features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}
class KddcupConfig(datasets.BuilderConfig):
def __init__(self, **kwargs):
super(KddcupConfig, self).__init__(version=VERSION, **kwargs)
self.features = features_per_config[kwargs["name"]]
class Kddcup(datasets.GeneratorBasedBuilder):
# dataset versions
DEFAULT_CONFIG = "kddcup"
BUILDER_CONFIGS = [
KddcupConfig(name="kddcup", description="Kddcup for multiclass classification.")
]
def _info(self):
info = datasets.DatasetInfo(description=DESCRIPTION, citation=_CITATION, homepage=_HOMEPAGE,
features=features_per_config[self.config.name])
return info
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
downloads = dl_manager.download_and_extract(urls_per_split)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloads["train"]}),
]
def _generate_examples(self, filepath: str):
data = pandas.read_csv(filepath, header=None)
data.columns = _BASE_FEATURE_NAMES
data = self.preprocess(data)
for row_id, row in data.iterrows():
data_row = dict(row)
yield row_id, data_row
def preprocess(self, data: pandas.DataFrame) -> pandas.DataFrame:
for feature in _ENCODING_DICS:
encoding_function = partial(self.encode, feature)
data.loc[:, feature] = data[feature].apply(encoding_function)
return data[list(features_types_per_config[self.config.name].keys())]
def encode(self, feature, value):
if feature in _ENCODING_DICS:
return _ENCODING_DICS[feature][value]
raise ValueError(f"Unknown feature: {feature}")