402 lines
13 KiB
Python
402 lines
13 KiB
Python
"""KDDCUP 99 dataset.
|
|
|
|
A classic dataset for anomaly detection.
|
|
|
|
The dataset page is available from UCI Machine Learning Repository
|
|
|
|
https://archive.ics.uci.edu/ml/machine-learning-databases/kddcup99-mld/kddcup.data.gz
|
|
|
|
"""
|
|
|
|
import errno
|
|
import logging
|
|
import os
|
|
from gzip import GzipFile
|
|
from os.path import exists, join
|
|
|
|
import joblib
|
|
import numpy as np
|
|
|
|
from ..utils import Bunch, check_random_state
|
|
from ..utils import shuffle as shuffle_method
|
|
from ..utils._param_validation import StrOptions, validate_params
|
|
from . import get_data_home
|
|
from ._base import (
|
|
RemoteFileMetadata,
|
|
_convert_data_dataframe,
|
|
_fetch_remote,
|
|
load_descr,
|
|
)
|
|
|
|
# The original data can be found at:
|
|
# https://archive.ics.uci.edu/ml/machine-learning-databases/kddcup99-mld/kddcup.data.gz
|
|
ARCHIVE = RemoteFileMetadata(
|
|
filename="kddcup99_data",
|
|
url="https://ndownloader.figshare.com/files/5976045",
|
|
checksum="3b6c942aa0356c0ca35b7b595a26c89d343652c9db428893e7494f837b274292",
|
|
)
|
|
|
|
# The original data can be found at:
|
|
# https://archive.ics.uci.edu/ml/machine-learning-databases/kddcup99-mld/kddcup.data_10_percent.gz
|
|
ARCHIVE_10_PERCENT = RemoteFileMetadata(
|
|
filename="kddcup99_10_data",
|
|
url="https://ndownloader.figshare.com/files/5976042",
|
|
checksum="8045aca0d84e70e622d1148d7df782496f6333bf6eb979a1b0837c42a9fd9561",
|
|
)
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
@validate_params(
|
|
{
|
|
"subset": [StrOptions({"SA", "SF", "http", "smtp"}), None],
|
|
"data_home": [str, os.PathLike, None],
|
|
"shuffle": ["boolean"],
|
|
"random_state": ["random_state"],
|
|
"percent10": ["boolean"],
|
|
"download_if_missing": ["boolean"],
|
|
"return_X_y": ["boolean"],
|
|
"as_frame": ["boolean"],
|
|
},
|
|
prefer_skip_nested_validation=True,
|
|
)
|
|
def fetch_kddcup99(
|
|
*,
|
|
subset=None,
|
|
data_home=None,
|
|
shuffle=False,
|
|
random_state=None,
|
|
percent10=True,
|
|
download_if_missing=True,
|
|
return_X_y=False,
|
|
as_frame=False,
|
|
):
|
|
"""Load the kddcup99 dataset (classification).
|
|
|
|
Download it if necessary.
|
|
|
|
================= ====================================
|
|
Classes 23
|
|
Samples total 4898431
|
|
Dimensionality 41
|
|
Features discrete (int) or continuous (float)
|
|
================= ====================================
|
|
|
|
Read more in the :ref:`User Guide <kddcup99_dataset>`.
|
|
|
|
.. versionadded:: 0.18
|
|
|
|
Parameters
|
|
----------
|
|
subset : {'SA', 'SF', 'http', 'smtp'}, default=None
|
|
To return the corresponding classical subsets of kddcup 99.
|
|
If None, return the entire kddcup 99 dataset.
|
|
|
|
data_home : str or path-like, default=None
|
|
Specify another download and cache folder for the datasets. By default
|
|
all scikit-learn data is stored in '~/scikit_learn_data' subfolders.
|
|
|
|
.. versionadded:: 0.19
|
|
|
|
shuffle : bool, default=False
|
|
Whether to shuffle dataset.
|
|
|
|
random_state : int, RandomState instance or None, default=None
|
|
Determines random number generation for dataset shuffling and for
|
|
selection of abnormal samples if `subset='SA'`. Pass an int for
|
|
reproducible output across multiple function calls.
|
|
See :term:`Glossary <random_state>`.
|
|
|
|
percent10 : bool, default=True
|
|
Whether to load only 10 percent of the data.
|
|
|
|
download_if_missing : bool, default=True
|
|
If False, raise an OSError if the data is not locally available
|
|
instead of trying to download the data from the source site.
|
|
|
|
return_X_y : bool, default=False
|
|
If True, returns ``(data, target)`` instead of a Bunch object. See
|
|
below for more information about the `data` and `target` object.
|
|
|
|
.. versionadded:: 0.20
|
|
|
|
as_frame : bool, default=False
|
|
If `True`, returns a pandas Dataframe for the ``data`` and ``target``
|
|
objects in the `Bunch` returned object; `Bunch` return object will also
|
|
have a ``frame`` member.
|
|
|
|
.. versionadded:: 0.24
|
|
|
|
Returns
|
|
-------
|
|
data : :class:`~sklearn.utils.Bunch`
|
|
Dictionary-like object, with the following attributes.
|
|
|
|
data : {ndarray, dataframe} of shape (494021, 41)
|
|
The data matrix to learn. If `as_frame=True`, `data` will be a
|
|
pandas DataFrame.
|
|
target : {ndarray, series} of shape (494021,)
|
|
The regression target for each sample. If `as_frame=True`, `target`
|
|
will be a pandas Series.
|
|
frame : dataframe of shape (494021, 42)
|
|
Only present when `as_frame=True`. Contains `data` and `target`.
|
|
DESCR : str
|
|
The full description of the dataset.
|
|
feature_names : list
|
|
The names of the dataset columns
|
|
target_names: list
|
|
The names of the target columns
|
|
|
|
(data, target) : tuple if ``return_X_y`` is True
|
|
A tuple of two ndarray. The first containing a 2D array of
|
|
shape (n_samples, n_features) with each row representing one
|
|
sample and each column representing the features. The second
|
|
ndarray of shape (n_samples,) containing the target samples.
|
|
|
|
.. versionadded:: 0.20
|
|
"""
|
|
data_home = get_data_home(data_home=data_home)
|
|
kddcup99 = _fetch_brute_kddcup99(
|
|
data_home=data_home,
|
|
percent10=percent10,
|
|
download_if_missing=download_if_missing,
|
|
)
|
|
|
|
data = kddcup99.data
|
|
target = kddcup99.target
|
|
feature_names = kddcup99.feature_names
|
|
target_names = kddcup99.target_names
|
|
|
|
if subset == "SA":
|
|
s = target == b"normal."
|
|
t = np.logical_not(s)
|
|
normal_samples = data[s, :]
|
|
normal_targets = target[s]
|
|
abnormal_samples = data[t, :]
|
|
abnormal_targets = target[t]
|
|
|
|
n_samples_abnormal = abnormal_samples.shape[0]
|
|
# selected abnormal samples:
|
|
random_state = check_random_state(random_state)
|
|
r = random_state.randint(0, n_samples_abnormal, 3377)
|
|
abnormal_samples = abnormal_samples[r]
|
|
abnormal_targets = abnormal_targets[r]
|
|
|
|
data = np.r_[normal_samples, abnormal_samples]
|
|
target = np.r_[normal_targets, abnormal_targets]
|
|
|
|
if subset == "SF" or subset == "http" or subset == "smtp":
|
|
# select all samples with positive logged_in attribute:
|
|
s = data[:, 11] == 1
|
|
data = np.c_[data[s, :11], data[s, 12:]]
|
|
feature_names = feature_names[:11] + feature_names[12:]
|
|
target = target[s]
|
|
|
|
data[:, 0] = np.log((data[:, 0] + 0.1).astype(float, copy=False))
|
|
data[:, 4] = np.log((data[:, 4] + 0.1).astype(float, copy=False))
|
|
data[:, 5] = np.log((data[:, 5] + 0.1).astype(float, copy=False))
|
|
|
|
if subset == "http":
|
|
s = data[:, 2] == b"http"
|
|
data = data[s]
|
|
target = target[s]
|
|
data = np.c_[data[:, 0], data[:, 4], data[:, 5]]
|
|
feature_names = [feature_names[0], feature_names[4], feature_names[5]]
|
|
|
|
if subset == "smtp":
|
|
s = data[:, 2] == b"smtp"
|
|
data = data[s]
|
|
target = target[s]
|
|
data = np.c_[data[:, 0], data[:, 4], data[:, 5]]
|
|
feature_names = [feature_names[0], feature_names[4], feature_names[5]]
|
|
|
|
if subset == "SF":
|
|
data = np.c_[data[:, 0], data[:, 2], data[:, 4], data[:, 5]]
|
|
feature_names = [
|
|
feature_names[0],
|
|
feature_names[2],
|
|
feature_names[4],
|
|
feature_names[5],
|
|
]
|
|
|
|
if shuffle:
|
|
data, target = shuffle_method(data, target, random_state=random_state)
|
|
|
|
fdescr = load_descr("kddcup99.rst")
|
|
|
|
frame = None
|
|
if as_frame:
|
|
frame, data, target = _convert_data_dataframe(
|
|
"fetch_kddcup99", data, target, feature_names, target_names
|
|
)
|
|
|
|
if return_X_y:
|
|
return data, target
|
|
|
|
return Bunch(
|
|
data=data,
|
|
target=target,
|
|
frame=frame,
|
|
target_names=target_names,
|
|
feature_names=feature_names,
|
|
DESCR=fdescr,
|
|
)
|
|
|
|
|
|
def _fetch_brute_kddcup99(data_home=None, download_if_missing=True, percent10=True):
|
|
"""Load the kddcup99 dataset, downloading it if necessary.
|
|
|
|
Parameters
|
|
----------
|
|
data_home : str, default=None
|
|
Specify another download and cache folder for the datasets. By default
|
|
all scikit-learn data is stored in '~/scikit_learn_data' subfolders.
|
|
|
|
download_if_missing : bool, default=True
|
|
If False, raise an OSError if the data is not locally available
|
|
instead of trying to download the data from the source site.
|
|
|
|
percent10 : bool, default=True
|
|
Whether to load only 10 percent of the data.
|
|
|
|
Returns
|
|
-------
|
|
dataset : :class:`~sklearn.utils.Bunch`
|
|
Dictionary-like object, with the following attributes.
|
|
|
|
data : ndarray of shape (494021, 41)
|
|
Each row corresponds to the 41 features in the dataset.
|
|
target : ndarray of shape (494021,)
|
|
Each value corresponds to one of the 21 attack types or to the
|
|
label 'normal.'.
|
|
feature_names : list
|
|
The names of the dataset columns
|
|
target_names: list
|
|
The names of the target columns
|
|
DESCR : str
|
|
Description of the kddcup99 dataset.
|
|
|
|
"""
|
|
|
|
data_home = get_data_home(data_home=data_home)
|
|
dir_suffix = "-py3"
|
|
|
|
if percent10:
|
|
kddcup_dir = join(data_home, "kddcup99_10" + dir_suffix)
|
|
archive = ARCHIVE_10_PERCENT
|
|
else:
|
|
kddcup_dir = join(data_home, "kddcup99" + dir_suffix)
|
|
archive = ARCHIVE
|
|
|
|
samples_path = join(kddcup_dir, "samples")
|
|
targets_path = join(kddcup_dir, "targets")
|
|
available = exists(samples_path)
|
|
|
|
dt = [
|
|
("duration", int),
|
|
("protocol_type", "S4"),
|
|
("service", "S11"),
|
|
("flag", "S6"),
|
|
("src_bytes", int),
|
|
("dst_bytes", int),
|
|
("land", int),
|
|
("wrong_fragment", int),
|
|
("urgent", int),
|
|
("hot", int),
|
|
("num_failed_logins", int),
|
|
("logged_in", int),
|
|
("num_compromised", int),
|
|
("root_shell", int),
|
|
("su_attempted", int),
|
|
("num_root", int),
|
|
("num_file_creations", int),
|
|
("num_shells", int),
|
|
("num_access_files", int),
|
|
("num_outbound_cmds", int),
|
|
("is_host_login", int),
|
|
("is_guest_login", int),
|
|
("count", int),
|
|
("srv_count", int),
|
|
("serror_rate", float),
|
|
("srv_serror_rate", float),
|
|
("rerror_rate", float),
|
|
("srv_rerror_rate", float),
|
|
("same_srv_rate", float),
|
|
("diff_srv_rate", float),
|
|
("srv_diff_host_rate", float),
|
|
("dst_host_count", int),
|
|
("dst_host_srv_count", int),
|
|
("dst_host_same_srv_rate", float),
|
|
("dst_host_diff_srv_rate", float),
|
|
("dst_host_same_src_port_rate", float),
|
|
("dst_host_srv_diff_host_rate", float),
|
|
("dst_host_serror_rate", float),
|
|
("dst_host_srv_serror_rate", float),
|
|
("dst_host_rerror_rate", float),
|
|
("dst_host_srv_rerror_rate", float),
|
|
("labels", "S16"),
|
|
]
|
|
|
|
column_names = [c[0] for c in dt]
|
|
target_names = column_names[-1]
|
|
feature_names = column_names[:-1]
|
|
|
|
if available:
|
|
try:
|
|
X = joblib.load(samples_path)
|
|
y = joblib.load(targets_path)
|
|
except Exception as e:
|
|
raise OSError(
|
|
"The cache for fetch_kddcup99 is invalid, please delete "
|
|
f"{str(kddcup_dir)} and run the fetch_kddcup99 again"
|
|
) from e
|
|
|
|
elif download_if_missing:
|
|
_mkdirp(kddcup_dir)
|
|
logger.info("Downloading %s" % archive.url)
|
|
_fetch_remote(archive, dirname=kddcup_dir)
|
|
DT = np.dtype(dt)
|
|
logger.debug("extracting archive")
|
|
archive_path = join(kddcup_dir, archive.filename)
|
|
file_ = GzipFile(filename=archive_path, mode="r")
|
|
Xy = []
|
|
for line in file_.readlines():
|
|
line = line.decode()
|
|
Xy.append(line.replace("\n", "").split(","))
|
|
file_.close()
|
|
logger.debug("extraction done")
|
|
os.remove(archive_path)
|
|
|
|
Xy = np.asarray(Xy, dtype=object)
|
|
for j in range(42):
|
|
Xy[:, j] = Xy[:, j].astype(DT[j])
|
|
|
|
X = Xy[:, :-1]
|
|
y = Xy[:, -1]
|
|
# XXX bug when compress!=0:
|
|
# (error: 'Incorrect data length while decompressing[...] the file
|
|
# could be corrupted.')
|
|
|
|
joblib.dump(X, samples_path, compress=0)
|
|
joblib.dump(y, targets_path, compress=0)
|
|
else:
|
|
raise OSError("Data not found and `download_if_missing` is False")
|
|
|
|
return Bunch(
|
|
data=X,
|
|
target=y,
|
|
feature_names=feature_names,
|
|
target_names=[target_names],
|
|
)
|
|
|
|
|
|
def _mkdirp(d):
|
|
"""Ensure directory d exists (like mkdir -p on Unix)
|
|
No guarantee that the directory is writable.
|
|
"""
|
|
try:
|
|
os.makedirs(d)
|
|
except OSError as e:
|
|
if e.errno != errno.EEXIST:
|
|
raise
|