name: Abnormally High AWS Instances Launched by User
id: 2a9b80d3-6340-4345-b5ad-290bf5d0dac4
version: 2
date: '2020-07-21'
author: Bhavin Patel, Splunk
type: batch
datamodel: []
description: This search looks for CloudTrail events where a user successfully launches
  an abnormally high number of instances. This search is deprecated and have been
  translated to use the latest Change Datamodel
search: '`cloudtrail` eventName=RunInstances errorCode=success | bucket span=10m _time
  | stats count AS instances_launched by _time userName | eventstats avg(instances_launched)
  as total_launched_avg, stdev(instances_launched) as total_launched_stdev | eval
  threshold_value = 4 | eval isOutlier=if(instances_launched > total_launched_avg+(total_launched_stdev
  * threshold_value), 1, 0) | search isOutlier=1 AND _time >= relative_time(now(),
  "-10m@m") | eval num_standard_deviations_away = round(abs(instances_launched - total_launched_avg)
  / total_launched_stdev, 2) | table _time, userName, instances_launched, num_standard_deviations_away,
  total_launched_avg, total_launched_stdev | `abnormally_high_aws_instances_launched_by_user_filter`'
how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
  and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
  inputs. The threshold value should be tuned to your environment.
known_false_positives: Many service accounts configured within an AWS infrastructure
  are known to exhibit this behavior. Please adjust the threshold values and filter
  out service accounts from the output. Always verify if this search alerted on a
  human user.
references: []
tags:
  analytic_story:
  - AWS Cryptomining
  - Suspicious AWS EC2 Activities
  asset_type: AWS Instance
  cis20:
  - CIS 13
  kill_chain_phases:
  - Actions on Objectives
  mitre_attack_id:
  - T1078.004
  nist:
  - DE.DP
  - DE.AE
  product:
  - Splunk Enterprise
  - Splunk Enterprise Security
  - Splunk Cloud
  required_fields:
  - _time
  - eventName
  - errorCode
  - userName
  risk_object: userName
  risk_object_type: user
  risk_score: 40
  security_domain: network
