:warning: THIS IS A EXPERIMENTAL DETECTION

This detection has been marked experimental by the Splunk Threat Research team. This means we have not been able to test, simulate, or build datasets for this detection. Use at your own risk. This analytic is NOT supported.

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Description

This search will detect spike in blocked outbound network connections originating from within your AWS environment. It will also update the cache file that factors in the latest data.

  • Type: Anomaly
  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud

  • Last Updated: 2018-05-07
  • Author: Bhavin Patel, Splunk
  • ID: d3fffa37-492f-487b-a35d-c60fcb2acf01

Annotations

ATT&CK
Kill Chain Phase
  • Actions on Objectives
  • Command & Control
NIST
  • DE.AE
  • DE.CM
  • PR.AC
CIS20
  • CIS 11
CVE
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`cloudwatchlogs_vpcflow` action=blocked (src_ip=10.0.0.0/8 OR src_ip=172.16.0.0/12 OR src_ip=192.168.0.0/16) ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16)  [search  `cloudwatchlogs_vpcflow` action=blocked (src_ip=10.0.0.0/8 OR src_ip=172.16.0.0/12 OR src_ip=192.168.0.0/16) ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16)  
| stats count as numberOfBlockedConnections by src_ip 
| inputlookup baseline_blocked_outbound_connections append=t 
| fields - latestCount 
| stats values(*) as * by src_ip 
| rename numberOfBlockedConnections as latestCount 
| eval newAvgBlockedConnections=avgBlockedConnections + (latestCount-avgBlockedConnections)/720 
| eval newStdevBlockedConnections=sqrt(((pow(stdevBlockedConnections, 2)*719 + (latestCount-newAvgBlockedConnections)*(latestCount-avgBlockedConnections))/720)) 
| eval avgBlockedConnections=coalesce(newAvgBlockedConnections, avgBlockedConnections), stdevBlockedConnections=coalesce(newStdevBlockedConnections, stdevBlockedConnections), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) 
| table src_ip, latestCount, numDataPoints, avgBlockedConnections, stdevBlockedConnections 
| outputlookup baseline_blocked_outbound_connections 
| eval dataPointThreshold = 5, deviationThreshold = 3 
| eval isSpike=if((latestCount > avgBlockedConnections+deviationThreshold*stdevBlockedConnections) AND numDataPoints > dataPointThreshold, 1, 0) 
| where isSpike=1 
| table src_ip] 
| stats values(dest_ip) as "Blocked Destination IPs", values(interface_id) as "resourceId" count as numberOfBlockedConnections, dc(dest_ip) as uniqueDestConnections by src_ip 
| `detect_spike_in_blocked_outbound_traffic_from_your_aws_filter`

Macros

The SPL above uses the following Macros:

:information_source: detect_spike_in_blocked_outbound_traffic_from_your_aws_filter is a empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.

Lookups

The SPL above uses the following Lookups:

Required fields

List of fields required to use this analytic.

  • _time
  • action
  • src_ip
  • dest_ip

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 VPC Flow logs. You can modify dataPointThreshold and deviationThreshold to better fit your environment. The dataPointThreshold variable is the number of data points required to meet the definition of "spike." The deviationThreshold variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of Blocked Outbound Connection" support search once to create a history of previously seen blocked outbound connections.

Known False Positives

The false-positive rate may vary based on the values ofdataPointThreshold and deviationThreshold. Additionally, false positives may result when AWS administrators roll out policies enforcing network blocks, causing sudden increases in the number of blocked outbound connections.

Associated Analytic Story

RBA

Risk Score Impact Confidence Message
25.0 50 50 tbd

:information_source: The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.

Reference

Test Dataset

Replay any dataset to Splunk Enterprise by using our replay.py tool or the UI. Alternatively you can replay a dataset into a Splunk Attack Range

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