Techniques
Sample rules
Abnormally High Number Of Cloud Instances Launched
- source: splunk
- technicques:
- T1078.004
Description
The following analytic detects an abnormally high number of cloud instances launched within a 4-hour period. It leverages cloud infrastructure logs and applies a probability density model to identify outliers based on historical data. This activity is significant for a SOC because a sudden spike in instance creation could indicate unauthorized access or misuse of cloud resources. If confirmed malicious, this behavior could lead to resource exhaustion, increased costs, or provide attackers with additional compute resources to further their objectives.
Detection logic
| tstats count as instances_launched values(All_Changes.object_id) as object_id from datamodel=Change where (All_Changes.action=created) AND All_Changes.status=success AND All_Changes.object_category=instance 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 HourOfDay isWeekend [summary cloud_excessive_instances_created_v1]
| where cardinality >=16
| apply cloud_excessive_instances_created_v1 threshold=0.005
| rename "IsOutlier(instances_launched)" as isOutlier
| where isOutlier=1
| eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0)
| eval distance_from_threshold = instances_launched - expected_upper_threshold
| table _time, user, instances_launched, expected_upper_threshold, distance_from_threshold, object_id
| `abnormally_high_number_of_cloud_instances_launched_filter`