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Description

This search looks for an increase of data transfers from your email server to your clients. This could be indicative of a malicious actor collecting data using your email server.

  • Type: Anomaly
  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
  • Datamodel: Network_Traffic
  • Last Updated: 2020-07-21
  • Author: Bhavin Patel, Splunk
  • ID: 7f5fb3e1-4209-4914-90db-0ec21b556368

ATT&CK

ID Technique Tactic
T1114.002 Remote Email Collection Collection
T1114 Email Collection Collection

| tstats `security_content_summariesonly` sum(All_Traffic.bytes_in) as bytes_in from datamodel=Network_Traffic where All_Traffic.dest_category=email_server by All_Traffic.src_ip _time span=1d 
| `drop_dm_object_name("All_Traffic")` 
| eventstats avg(bytes_in) as avg_bytes_in stdev(bytes_in) as stdev_bytes_in 
| eventstats count as num_data_samples avg(eval(if(_time < relative_time(now(), "@d"), bytes_in, null))) as per_source_avg_bytes_in stdev(eval(if(_time < relative_time(now(), "@d"), bytes_in, null))) as per_source_stdev_bytes_in by src_ip 
| eval minimum_data_samples = 4, deviation_threshold = 3 
| where num_data_samples >= minimum_data_samples AND bytes_in > (avg_bytes_in + (deviation_threshold * stdev_bytes_in)) AND bytes_in > (per_source_avg_bytes_in + (deviation_threshold * per_source_stdev_bytes_in)) AND _time >= relative_time(now(), "@d") 
| eval num_standard_deviations_away_from_server_average = round(abs(bytes_in - avg_bytes_in) / stdev_bytes_in, 2), num_standard_deviations_away_from_client_average = round(abs(bytes_in - per_source_avg_bytes_in) / per_source_stdev_bytes_in, 2) 
| table src_ip, _time, bytes_in, avg_bytes_in, per_source_avg_bytes_in, num_standard_deviations_away_from_server_average, num_standard_deviations_away_from_client_average 
| `hosts_receiving_high_volume_of_network_traffic_from_email_server_filter`

Associated Analytic Story

How To Implement

This search requires you to be ingesting your network traffic and populating the Network_Traffic data model. Your email servers must be categorized as "email_server" for the search to work, as well. You may need to adjust the deviation_threshold and minimum_data_samples values based on the network traffic in your environment. The "deviation_threshold" field is a multiplying factor to control how much variation you're willing to tolerate. The "minimum_data_samples" field is the minimum number of connections of data samples required for the statistic to be valid.

Required field

  • _time
  • All_Traffic.bytes_in
  • All_Traffic.dest_category
  • All_Traffic.src_ip

Kill Chain Phase

  • Actions on Objectives

Known False Positives

The false-positive rate will vary based on how you set the deviation_threshold and data_samples values. Our recommendation is to adjust these values based on your network traffic to and from your email servers.

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

source | version: 2