S1SuperTimeline
A command line tool that creates a super timeline from SentinelOne's Deep Visibility data
What does it do?
The script accepts a S1QL query and returns a XLSX document with all the data. The script has mulithreading capabilities and allows the user to break up queries by minute increments. This method automates downloading datasets that are over 20K records (Deep Visibility's limit). For example, a hosts entire deep visbility history could be downloaded using this script. Assuming you do not go over 1,048,576 records (xlsx limit).
How to run it
Install dependencies
pip install -r requirements.txt
Run
# Hour Increments (60 min)
python3 s1_supertimeline.py -t my_api_token -url sentinelone.com -from 2020-01-01T00:00 -to 2020-01-01T12:30 -min 60
Help Page
python3 s1_supertimeline.py -h
usage: s1_supertimeline.py [-h] -t S1_API_TOKEN -url S1_URL -from FROM_DATE -to TO_DATE -min MIN_INCREMENTS [-u]
SentinelOne SuperTimeline :: By Juan Ortega
options:
-h, --help show this help message and exit
Required Arguments:
-t S1_API_TOKEN, --s1_api_token S1_API_TOKEN
SentinelOne API Token
-url S1_URL, --s1_url S1_URL
SentinelOne Console Url
-from FROM_DATE, --from_date FROM_DATE
From Date. Format YYYY-MM-DDTHH:MM or YYYY-MM-DD
-to TO_DATE, --to_date TO_DATE
To Date. Format YYYY-MM-DDTHH:MM or YYYY-MM-DD
-min MIN_INCREMENTS, --min_increments MIN_INCREMENTS
Minute increments to split time date range by
-u, --utc Accepts --date_from/--date_to as UTC, Default is local time
Troubleshooting
If you have issues running the script. Try installing tablib like this:
pip install "tablib['xlsx']"
3 Oct 12, 2022
5 Nov 06, 2022
32 Jun 23, 2022
1 Nov 29, 2021
1 Jan 13, 2022
8 May 06, 2022
13 Nov 28, 2022
4 Sep 04, 2021
12.6k Dec 30, 2022
45 Dec 11, 2022
4 Nov 02, 2021
4 Dec 22, 2021
1 Mar 27, 2022
1 Nov 03, 2021
2 Jan 05, 2022
7 Jun 21, 2022
33 Dec 14, 2022
1 Jan 03, 2022
56 Nov 10, 2022
28 Nov 06, 2022