95 lines
4.0 KiB
ReStructuredText
95 lines
4.0 KiB
ReStructuredText
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.. _kddcup99_dataset:
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Kddcup 99 dataset
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-----------------
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The KDD Cup '99 dataset was created by processing the tcpdump portions
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of the 1998 DARPA Intrusion Detection System (IDS) Evaluation dataset,
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created by MIT Lincoln Lab [2]_. The artificial data (described on the `dataset's
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homepage <https://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html>`_) was
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generated using a closed network and hand-injected attacks to produce a
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large number of different types of attack with normal activity in the
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background. As the initial goal was to produce a large training set for
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supervised learning algorithms, there is a large proportion (80.1%) of
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abnormal data which is unrealistic in real world, and inappropriate for
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unsupervised anomaly detection which aims at detecting 'abnormal' data, i.e.:
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* qualitatively different from normal data
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* in large minority among the observations.
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We thus transform the KDD Data set into two different data sets: SA and SF.
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* SA is obtained by simply selecting all the normal data, and a small
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proportion of abnormal data to gives an anomaly proportion of 1%.
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* SF is obtained as in [3]_
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by simply picking up the data whose attribute logged_in is positive, thus
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focusing on the intrusion attack, which gives a proportion of 0.3% of
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attack.
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* http and smtp are two subsets of SF corresponding with third feature
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equal to 'http' (resp. to 'smtp').
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General KDD structure :
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================ ==========================================
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Samples total 4898431
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Dimensionality 41
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Features discrete (int) or continuous (float)
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Targets str, 'normal.' or name of the anomaly type
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================ ==========================================
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SA structure :
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================ ==========================================
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Samples total 976158
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Dimensionality 41
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Features discrete (int) or continuous (float)
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Targets str, 'normal.' or name of the anomaly type
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================ ==========================================
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SF structure :
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================ ==========================================
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Samples total 699691
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Dimensionality 4
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Features discrete (int) or continuous (float)
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Targets str, 'normal.' or name of the anomaly type
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================ ==========================================
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http structure :
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================ ==========================================
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Samples total 619052
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Dimensionality 3
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Features discrete (int) or continuous (float)
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Targets str, 'normal.' or name of the anomaly type
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================ ==========================================
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smtp structure :
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================ ==========================================
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Samples total 95373
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Dimensionality 3
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Features discrete (int) or continuous (float)
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Targets str, 'normal.' or name of the anomaly type
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================ ==========================================
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:func:`sklearn.datasets.fetch_kddcup99` will load the kddcup99 dataset; it
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returns a dictionary-like object with the feature matrix in the ``data`` member
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and the target values in ``target``. The "as_frame" optional argument converts
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``data`` into a pandas DataFrame and ``target`` into a pandas Series. The
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dataset will be downloaded from the web if necessary.
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.. topic:: References
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.. [2] Analysis and Results of the 1999 DARPA Off-Line Intrusion
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Detection Evaluation, Richard Lippmann, Joshua W. Haines,
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David J. Fried, Jonathan Korba, Kumar Das.
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.. [3] K. Yamanishi, J.-I. Takeuchi, G. Williams, and P. Milne. Online
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unsupervised outlier detection using finite mixtures with
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discounting learning algorithms. In Proceedings of the sixth
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ACM SIGKDD international conference on Knowledge discovery
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and data mining, pages 320-324. ACM Press, 2000.
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