This search will detect a spike in the number of API calls made to your cloud infrastructure environment by a user.
- Type: Anomaly
- Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Datamodel: Change
- Last Updated: 2020-09-07
- Author: David Dorsey, Splunk
- ID: 0840ddf1-8c89-46ff-b730-c8d6722478c0
Kill Chain Phase
- Actions on Objectives
- CIS 16
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | tstats count as api_calls values(All_Changes.command) as command from datamodel=Change where All_Changes.user!=unknown All_Changes.status=success by All_Changes.user _time span=1h | `drop_dm_object_name("All_Changes")` | eval HourOfDay=strftime(_time, "%H") | eval HourOfDay=floor(HourOfDay/4)*4 | eval DayOfWeek=strftime(_time, "%w") | eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) | join user HourOfDay isWeekend [ summary cloud_excessive_api_calls_v1] | where cardinality >=16 | apply cloud_excessive_api_calls_v1 threshold=0.005 | rename "IsOutlier(api_calls)" as isOutlier | where isOutlier=1 | eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) | where api_calls > expected_upper_threshold | eval distance_from_threshold = api_calls - expected_upper_threshold | table _time, user, command, api_calls, expected_upper_threshold, distance_from_threshold | `abnormally_high_number_of_cloud_infrastructure_api_calls_filter`
The SPL above uses the following Macros:
abnormally_high_number_of_cloud_infrastructure_api_calls_filter is a empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.
List of fields required to use this analytic.
How To Implement
You must be ingesting your cloud infrastructure logs. You also must run the baseline search
Baseline Of Cloud Infrastructure API Calls Per User to create the probability density function.
Known False Positives
Associated Analytic Story
|15.0||30||50||user $user$ has made $api_calls$ api calls, violating the dynamic threshold of $expected_upper_threshold$ with the following command $command$.|
The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.
source | version: 1