fetch_kddcup99(subset=None, shuffle=False, random_state=None, percent10=True, download_if_missing=True)¶
Load and return the kddcup 99 dataset (classification).
The KDD Cup ‘99 dataset was created by processing the tcpdump portions of the 1998 DARPA Intrusion Detection System (IDS) Evaluation dataset, created by MIT Lincoln Lab  . The artificial data was generated using a closed network and hand-injected attacks to produce a large number of different types of attack with normal activity in the background. As the initial goal was to produce a large training set for supervised learning algorithms, there is a large proportion (80.1%) of abnormal data which is unrealistic in real world, and inappropriate for unsupervised anomaly detection which aims at detecting ‘abnormal’ data, ie
- qualitatively different from normal data.
- in large minority among the observations.
We thus transform the KDD Data set into two different data sets: SA and SF.
- SA is obtained by simply selecting all the normal data, and a small proportion of abnormal data to gives an anomaly proportion of 1%.
- SF is obtained as in  by simply picking up the data whose attribute logged_in is positive, thus focusing on the intrusion attack, which gives a proportion of 0.3% of attack.
- http and smtp are two subsets of SF corresponding with third feature equal to ‘http’ (resp. to ‘smtp’)
General KDD structure :
Samples total 4898431 Dimensionality 41 Features discrete (int) or continuous (float) Targets str, ‘normal.’ or name of the anomaly type
SA structure :
Samples total 976158 Dimensionality 41 Features discrete (int) or continuous (float) Targets str, ‘normal.’ or name of the anomaly type
SF structure :
Samples total 699691 Dimensionality 4 Features discrete (int) or continuous (float) Targets str, ‘normal.’ or name of the anomaly type
http structure :
Samples total 619052 Dimensionality 3 Features discrete (int) or continuous (float) Targets str, ‘normal.’ or name of the anomaly type
smtp structure :
Samples total 95373 Dimensionality 3 Features discrete (int) or continuous (float) Targets str, ‘normal.’ or name of the anomaly type
New in version 0.18.
subset : None, ‘SA’, ‘SF’, ‘http’, ‘smtp’
To return the corresponding classical subsets of kddcup 99. If None, return the entire kddcup 99 dataset.
random_state : int, RandomState instance or None, optional (default=None)
Random state for shuffling the dataset. If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.
shuffle : bool, default=False
Whether to shuffle dataset.
percent10 : bool, default=False
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.
data : Bunch
Dictionary-like object, the interesting attributes are: ‘data’, the data to learn and ‘target’, the regression target for each sample.
[R132] Analysis and Results of the 1999 DARPA Off-Line Intrusion Detection Evaluation Richard Lippmann, Joshua W. Haines, David J. Fried, Jonathan Korba, Kumar Das [R133] A Geometric Framework for Unsupervised Anomaly Detection: Detecting Intrusions in Unlabeled Data (2002) by Eleazar Eskin, Andrew Arnold, Michael Prerau, Leonid Portnoy, Sal Stolfo