Detection: Email servers sending high volume traffic to hosts

Description

The following analytic identifies a significant increase in data transfers from your email server to client hosts. It leverages the Network_Traffic data model to monitor outbound traffic from email servers, using statistical analysis to detect anomalies based on average and standard deviation metrics. This activity is significant as it may indicate a malicious actor exfiltrating data via your 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_out) as bytes_out from datamodel=Network_Traffic where All_Traffic.src_category=email_server by All_Traffic.dest_ip _time span=1d 
 3| `drop_dm_object_name("All_Traffic")` 
 4| eventstats avg(bytes_out) as avg_bytes_out stdev(bytes_out) as stdev_bytes_out 
 5| eventstats count as num_data_samples avg(eval(if(_time < relative_time(now(), "@d"), bytes_out, null))) as per_source_avg_bytes_out stdev(eval(if(_time < relative_time(now(), "@d"), bytes_out, null))) as per_source_stdev_bytes_out by dest_ip 
 6| eval minimum_data_samples = 4, deviation_threshold = 3 
 7| where num_data_samples >= minimum_data_samples AND bytes_out > (avg_bytes_out + (deviation_threshold * stdev_bytes_out)) AND bytes_out > (per_source_avg_bytes_out + (deviation_threshold * per_source_stdev_bytes_out)) AND _time >= relative_time(now(), "@d") 
 8| eval num_standard_deviations_away_from_server_average = round(abs(bytes_out - avg_bytes_out) / stdev_bytes_out, 2), num_standard_deviations_away_from_client_average = round(abs(bytes_out - per_source_avg_bytes_out) / per_source_stdev_bytes_out, 2) 
 9| table dest_ip, _time, bytes_out, avg_bytes_out, per_source_avg_bytes_out, num_standard_deviations_away_from_server_average, num_standard_deviations_away_from_client_average 
10| `email_servers_sending_high_volume_traffic_to_hosts_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

• HAFNIUM Group

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