- 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
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 fieldsland
,logged_in
,root_shell
,is_hot_login
andis_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 | int32 |
Supervised 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"
}