Detect Distributed Password Spray Attempts
Description
This analytic employs the 3-sigma approach to identify distributed password spray attacks. A distributed password spray attack is a type of brute force attack where the attacker attempts a few common passwords against many different accounts, connecting from multiple IP addresses to avoid detection. By utilizing the Authentication Data Model, this detection is effective for all CIM-mapped authentication events, providing comprehensive coverage and enhancing security against these attacks.
Query · spl
| tstats `security_content_summariesonly` dc(Authentication.user) AS unique_accounts dc(Authentication.src) as unique_src values(Authentication.app) as app values(Authentication.src) as src count(Authentication.user) as total_failures from datamodel=Authentication.Authentication where Authentication.action="failure" NOT Authentication.src IN ("-","unknown") Authentication.user_agent="*" by Authentication.signature_id, Authentication.user_agent, sourcetype, _time span=10m | `drop_dm_object_name("Authentication")` ```fill out time buckets for 0-count events during entire search length``` | appendpipe [| timechart limit=0 span=10m count | table _time] | fillnull value=0 unique_accounts, unique_src ``` Create aggregation field & apply to all null events``` | eval counter=sourcetype+"__"+signature_id | eventstats values(counter) as fnscounter | eval counter=coalesce(counter,fnscounter) | stats values(total_failures) as total_failures values(signature_id) as signature_id values(src) as src values(sourcetype) as sourcetype values(app) as app count by counter unique_accounts unique_src user_agent _time
``` remove 0 count rows where counter has data```
| sort - _time unique_accounts | dedup _time counter ``` 3-sigma detection logic ``` | eventstats avg(unique_accounts) as comp_avg_user , stdev(unique_accounts) as comp_std_user avg(unique_src) as comp_avg_src , stdev(unique_src) as comp_std_src by counter user_agent | eval upperBoundUser=(comp_avg_user+comp_std_user*3), upperBoundsrc=(comp_avg_src+comp_std_src*3) | eval isOutlier=if((unique_accounts > 30 and unique_accounts >= upperBoundUser) and (unique_src > 30 and unique_src >= upperBoundsrc), 1, 0) | replace "::ffff:*" with * in src | where isOutlier=1 | foreach *
[ eval <<FIELD>> = if(<<FIELD>>="null",null(),<<FIELD>>)]
| mvexpand src | iplocation src | table _time, unique_src, unique_accounts, total_failures, sourcetype, signature_id, user_agent, src, Country | eval date_wday=strftime(_time,"%a"), date_hour=strftime(_time,"%H") | `detect_distributed_password_spray_attempts_filter`
Implementation guide
Ensure that all relevant authentication data is mapped to the Common Information Model (CIM) and that the src field is populated with the source device information. Additionally, ensure that fill_nullvalue is set within the security_content_summariesonly macro to include authentication events from log sources that do not feature the signature_id field in the results.
Known false positives
- It is common to see a spike of legitimate failed authentication events on monday mornings.
Analyst notes
Known false positives: It is common to see a spike of legitimate failed authentication events on monday mornings.