/
_kddcup99.py
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/
_kddcup99.py
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"""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
from gzip import GzipFile
import logging
import os
from os.path import exists, join
import numpy as np
import joblib
from ._base import _fetch_remote
from ._base import _convert_data_dataframe
from . import get_data_home
from ._base import RemoteFileMetadata
from ._base import load_descr
from ..utils import Bunch
from ..utils import check_random_state
from ..utils import shuffle as shuffle_method
# 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__)
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, 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 a IOError 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
.. 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 a IOError 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 IOError(
"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 IOError("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