Name:Detect Password Spray Attack Behavior On User id:a7539705-7183-4a12-9b6a-b6eef645a6d7 version:2 date:2024-09-30 author:Steven Dick status:production type:TTP Description:The following analytic identifies any user failing to authenticate from 10 or more unique sources. This behavior could represent an adversary performing a Password Spraying attack to obtain initial access or elevate privileges. This logic can be used for real time security monitoring as well as threat hunting exercises. Environments can be very different depending on the organization. Test and customize this detections thresholds as needed Data_source:
-Authentication Events (various)
search:| tstats `security_content_summariesonly` max(_time) as lastTime, min(_time) as firstTime, values(Authentication.user_category) as user_category values(Authentication.src_category) as src_category values(Authentication.app) as app count from datamodel=Authentication.Authentication where * by Authentication.action,Authentication.src,Authentication.user | `drop_dm_object_name("Authentication")` | eval user=case((match(upper(user),"[a-zA-Z0-9]{3}")),upper(user),true(),null), success=if(action="success",count,0), src=upper(src), success_src=if(action="success",src,null), failure=if(action="failure",count,0), failed_src=if(action="failure",src,null) | `detect_password_spray_attack_behavior_on_user_filter` | stats count min(firstTime) as firstTime max(lastTime) as lastTime values(app) as app values(src_category) as src_category values(success_src) as src values(failed_src) as failed_src dc(success_src) as success_dc dc(failed_src) as failed_dc dc(src) as src_dc, sum(failure) as failure, sum(success) as success by user | fields - _time | where src_dc >= 10 AND .25 > (success/failure) AND failed_dc > success_dc | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
how_to_implement:This detection requires ingesting authentication data to the appropriate accelerated datamodel. Recommend adjusting the search time window for this correlation to match the number of unique users (user_dc) in hours. i.e. 10 users over 10hrs known_false_positives:Domain controllers, authentication chokepoints, and vulnerability scanners. References: -https://attack.mitre.org/techniques/T1110/003/ -https://www.microsoft.com/en-us/security/blog/2020/04/23/protecting-organization-password-spray-attacks/ -https://github.com/MarkoH17/Spray365 drilldown_searches: name:'View the detection results for - "$src$" and "$user$"' search:'%original_detection_search% | search src = "$src$" user = "$user$"' earliest_offset:'$info_min_time$' latest_offset:'$info_max_time$' name:'View risk events for the last 7 days for - "$src$" and "$user$"' search:'| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' earliest_offset:'$info_min_time$' latest_offset:'$info_max_time$' tags: analytic_story: - 'Compromised User Account' asset_type:Account confidence:75 impact:80 message:A total of $src_dc$ distinct sources attempted to access the account [$user$], $count$ times between [$firstTime$] and [$lastTime$]. $success$ successful logins detected. mitre_attack_id: - 'T1110.003' - 'T1110' observable: name:'src' type:'Hostname' - role: - 'Victim' name:'user' type:'User' - role: - 'Victim' product: - 'Splunk Enterprise' - 'Splunk Enterprise Security' - 'Splunk Cloud' required_fields: - '_time' - 'Authentication.user_category' - 'Authentication.src_category' - 'Authentication.app' - 'Authentication.action' - 'Authentication.src' - 'Authentication.user' risk_score:60 security_domain:access
