Detection: Hosts receiving high volume of network traffic from email server

Description

The following analytic identifies hosts receiving an unusually high volume of network traffic from an email server. It leverages the Network_Traffic data model to sum incoming bytes to clients from email servers, comparing current traffic against historical averages and standard deviations. This activity is significant as it may indicate data exfiltration by a malicious actor using the email server. If confirmed malicious, this could lead to unauthorized data access and potential data breaches, compromising sensitive information and impacting organizational security.

Search

 1
 2| 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 
 3| `drop_dm_object_name("All_Traffic")` 
 4| eventstats avg(bytes_in) as avg_bytes_in stdev(bytes_in) as stdev_bytes_in 
 5| 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 
 6| eval minimum_data_samples = 4, deviation_threshold = 3 
 7| 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") 
 8| 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) 
 9| 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 
10| `hosts_receiving_high_volume_of_network_traffic_from_email_server_filter`

Data Source

Macros Used

Annotations

Default Configuration

This detection is configured by default in Splunk Enterprise Security to run with the following settings:

Implementation

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.

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.

Associated Analytic Story

• Collection and Staging

Risk Based Analytics (RBA)

Detection Testing

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: GitHub | Version: 3