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venv/Lib/site-packages/sklearn/datasets/descr/kddcup99.rst

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+.. _kddcup99_dataset:
+
+Kddcup 99 dataset
+-----------------
+
+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 [1]. The artificial data (described on the `dataset's
+homepage <https://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html>`_) 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
+
+1) qualitatively different from normal data
+
+2) 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 [2]
+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
+    ================      ==========================================
+
+:func:`sklearn.datasets.fetch_kddcup99` will load the kddcup99 dataset; it
+returns a dictionary-like object with the feature matrix in the ``data`` member
+and the target values in ``target``. The dataset will be downloaded from the
+web if necessary.
+
+.. topic: References
+
+    .. [1] Analysis and Results of the 1999 DARPA Off-Line Intrusion
+           Detection Evaluation Richard Lippmann, Joshua W. Haines,
+           David J. Fried, Jonathan Korba, Kumar Das
+
+    .. [2] K. Yamanishi, J.-I. Takeuchi, G. Williams, and P. Milne. Online
+           unsupervised outlier detection using finite mixtures with
+           discounting learning algorithms. In Proceedings of the sixth
+           ACM SIGKDD international conference on Knowledge discovery
+           and data mining, pages 320-324. ACM Press, 2000.
+