kddcup99 |
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Description:
This is the data set used for The Third International Knowledge Discovery and Data Mining Tools Competition, which was held in conjunction with KDD-99 The Fifth International Conference on Knowledge Discovery and Data Mining. The competition task was to build a network intrusion detector, a predictive model capable of distinguishing between 'bad' connections, called intrusions or attacks, and 'good' normal connections. This database contains a standard set of data to be audited, which includes a wide variety of intrusions simulated in a military network environment. Additional Documentation: Explore on Papers With Code north_east Homepage: https://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html Source code: tfds.datasets.kddcup99.Builder Versions: 1.0.0: Initial release. 1.0.1 (default): Fixes parsing of boolean fields land, logged_in, root_shell, is_hot_login and is_guest_login.Download size: 18.62 MiB Dataset size: 5.25 GiB Auto-cached (documentation): No Splits: Split Examples 'test' 311,029 'train' 4,898,431 Feature structure: FeaturesDict({ 'count': int32, 'diff_srv_rate': float32, 'dst_bytes': int32, 'dst_host_count': int32, 'dst_host_diff_srv_rate': float32, 'dst_host_rerror_rate': float32, 'dst_host_same_src_port_rate': float32, 'dst_host_same_srv_rate': float32, 'dst_host_serror_rate': float32, 'dst_host_srv_count': int32, 'dst_host_srv_diff_host_rate': float32, 'dst_host_srv_rerror_rate': float32, 'dst_host_srv_serror_rate': float32, 'duration': int32, 'flag': ClassLabel(shape=(), dtype=int64, num_classes=11), 'hot': int32, 'is_guest_login': bool, 'is_hot_login': bool, 'label': ClassLabel(shape=(), dtype=int64, num_classes=40), 'land': bool, 'logged_in': bool, 'num_access_files': int32, 'num_compromised': int32, 'num_failed_logins': int32, 'num_file_creations': int32, 'num_outbound_cmds': int32, 'num_root': int32, 'num_shells': int32, 'protocol_type': ClassLabel(shape=(), dtype=int64, num_classes=3), 'rerror_rate': float32, 'root_shell': bool, 'same_srv_rate': float32, 'serror_rate': float32, 'service': ClassLabel(shape=(), dtype=int64, num_classes=71), 'src_bytes': int32, 'srv_count': int32, 'srv_diff_host_rate': float32, 'srv_rerror_rate': float32, 'srv_serror_rate': float32, 'su_attempted': int32, 'urgent': int32, 'wrong_fragment': int32, }) Feature documentation: Feature Class Shape Dtype Description FeaturesDict count Tensor int32 diff_srv_rate Tensor float32 dst_bytes Tensor int32 dst_host_count Tensor int32 dst_host_diff_srv_rate Tensor float32 dst_host_rerror_rate Tensor float32 dst_host_same_src_port_rate Tensor float32 dst_host_same_srv_rate Tensor float32 dst_host_serror_rate Tensor float32 dst_host_srv_count Tensor int32 dst_host_srv_diff_host_rate Tensor float32 dst_host_srv_rerror_rate Tensor float32 dst_host_srv_serror_rate Tensor float32 duration Tensor int32 flag ClassLabel int64 hot Tensor int32 is_guest_login Tensor bool is_hot_login Tensor bool label ClassLabel int64 land Tensor bool logged_in Tensor bool num_access_files Tensor int32 num_compromised Tensor int32 num_failed_logins Tensor int32 num_file_creations Tensor int32 num_outbound_cmds Tensor int32 num_root Tensor int32 num_shells Tensor int32 protocol_type ClassLabel int64 rerror_rate Tensor float32 root_shell Tensor bool same_srv_rate Tensor float32 serror_rate Tensor float32 service ClassLabel int64 src_bytes Tensor int32 srv_count Tensor int32 srv_diff_host_rate Tensor float32 srv_rerror_rate Tensor float32 srv_serror_rate Tensor float32 su_attempted Tensor int32 urgent Tensor int32 wrong_fragment Tensor int32Supervised keys (See as_supervised doc): None Figure (tfds.show_examples): Not supported. Examples (tfds.as_dataframe): Citation: @misc{Dua:2019 , author = "Dua, Dheeru and Graff, Casey", year = 2017, title = "{UCI} Machine Learning Repository", url = "http://archive.ics.uci.edu/ml", institution = "University of California, Irvine, School of Information and Computer Sciences" } |
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