Name:Detect Distributed Password Spray Attempts id:b1a82fc8-8a9f-4344-9ec2-bde5c5331b57 version:2 date:2024-10-17 author:Dean Luxton status:production type:Hunting 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. Data_source:
-Azure Active Directory Sign-in activity
search:| 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`
how_to_implement: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. References: -https://attack.mitre.org/techniques/T1110/003/ drilldown_searches:
: tags: analytic_story: - 'Compromised User Account' - 'Active Directory Password Spraying' asset_type:Endpoint atomic_guid: - '90bc2e54-6c84-47a5-9439-0a2a92b4b175' confidence:70 impact:70 message:Distributed Password Spray Attempt Detected from $src$ mitre_attack_id: - 'T1110.003' - 'T1110' observable: name:'src' type:'IP Address' - role: - 'Attacker' name:'user_agent' type:'Other' - role: - 'Attacker' product: - 'Splunk Enterprise' - 'Splunk Enterprise Security' - 'Splunk Cloud' risk_score:49 required_fields: - 'Authentication.user' - 'Authentication.src' security_domain:access manual_test:The dataset & hardcoded timerange doesn't meet the criteria for this detection.
