公开数据集
数据结构 ? 11.7M
Data Structure ?
* 以上分析是由系统提取分析形成的结果,具体实际数据为准。
README.md
Dataset creator and donator: Ken Montanez email: kenmonta[at]cal.berkeley.edu institution: Information Security, Amazon Corp.
Data Set Information:
This is a sparse data set, less than 10% of the attributes are used for each sample. The link is to a '*.tgz' file which contains two files:
[amzn-anon-access-samples-2.0.csv] this file contains the access for users
[amzn-anon-access-samples-history-2.0.csv] this file contains the access history for a given user
Attribute Information:
__amzn-anon-access-samples-2.0.csv__
This is a sparse data set containing users and their assigned access. The file contains 4 categories of attributes.
1) [PERSON_{ATTRIBUTE}] This category describes the 'user' who was given access. The [PERSON_ID] column is the primary key column for the file. There is one row per user.
PERSON_ID: id of the user
PERSON_MGR_ID: id of the user's manager
PERSON_ROLLUP_1: user grouping id
PERSON_ROLLUP_2: user grouping id
PERSON_ROLLUP_3: user grouping id
PERSON_DEPTNAME: department desciption id
PERSON_LOCATION: region id
PERSON_BUSINESS_TITLE: title id
PERSON_BUSINESS_TITLE_DETAIL: description id
PERSON_JOB_CODE: job code id
PERSON_COMPANY: company id
PERSON_JOB_FAMILY: job family id
2) [RESOURCE_{ID}] This category of attributes are the resources that a users can possibly have access to. A user will have a 1 in this column if the have access to it otherwise it will be 0.
3) [GROUP_{ID}] - This category of attributes are the groups that a users can possibly have access to. A user will have a 1 in this column if the have access to it otherwise it will be 0.
4) [SYSTEM_SUPPORT_{ID}] - This category of attributes are the system that a user can possibly be supporting. A user will have a 1 in this column if the have can possibly be supporting it, otherwise it will be 0.
__amzn-anon-access-samples-history-2.0.csv__
Permissions Time series data. Here is a short description of the columns:
ACTION: either 'remove_access' or 'add_access'
TARGET_NAME: either the {RESOURCE_ID} or {GROUP_ID}
LOGIN: the id of the user that is obtaining or losing access
REQUEST_DATE: YYYY-MM-DD HH:MM:SS
AUTHORIZATION_DATE: YYYY-MM-DD HH:MM:SS
Relevant Papers:
N/A
Citation Request:
Please refer to the Machine Learning Repository's citation policy.
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