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| name: AWS CloudWatchLogs VPCflow | ||
| id: 38a34fc4-e128-4478-a8f4-7835d51d5135 | ||
| author: Bhavin Patel, Splunk | ||
| source: aws_cloudwatchlogs_vpcflow | ||
| sourcetype: aws:cloudwatchlogs:vpcflow | ||
| separator: eventName | ||
| supported_TA: | ||
| name: Splunk Add-on for Amazon Web Services (AWS) | ||
| version: 7.4.1 | ||
| url: https://splunkbase.splunk.com/app/1876 | ||
| event_names: [] | ||
| fields: | ||
| - _raw | ||
| - _time | ||
| - account_id | ||
| - action | ||
| - app | ||
| - aws_account_id | ||
| - bytes | ||
| - date_hour | ||
| - date_mday | ||
| - date_minute | ||
| - date_month | ||
| - date_second | ||
| - date_wday | ||
| - date_year | ||
| - date_zone | ||
| - dest | ||
| - dest_ip | ||
| - dest_port | ||
| - duration | ||
| - dvc | ||
| - end_time | ||
| - eventtype | ||
| - host | ||
| - index | ||
| - interface_id | ||
| - linecount | ||
| - log_status | ||
| - packets | ||
| - protocol | ||
| - protocol_code | ||
| - protocol_full_name | ||
| - protocol_version | ||
| - punct | ||
| - region | ||
| - source | ||
| - sourcetype | ||
| - splunk_server | ||
| - splunk_server_group | ||
| - src | ||
| - src_ip | ||
| - src_port | ||
| - start_time | ||
| - tag | ||
| - tag::action | ||
| - tag::eventtype | ||
| - timeendpos | ||
| - timestartpos | ||
| - transport | ||
| - user_id | ||
| - vendor_account | ||
| - vendor_product | ||
| - version | ||
| - vpcflow_action | ||
| example_log: '2 123397614277 eni-0b0f9f261f45e6489 10.0.1.30 10.0.1.1 47254 22 17 2 98 1697608042 1697608070 ACCEPT OK' |
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88 changes: 88 additions & 0 deletions
88
data_sources/endpoint/event_sources/Windows_Event_Log_System_4728.yml
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| event_name: Windows Event Log System 4728 | ||
| fields: | ||
| - _time | ||
| - Account_Domain | ||
| - Account_Name | ||
| - CategoryString | ||
| - ComputerName | ||
| - Error_Code | ||
| - EventCode | ||
| - EventType | ||
| - Keywords | ||
| - LogName | ||
| - Logon_ID | ||
| - Message | ||
| - OpCode | ||
| - RecordNumber | ||
| - Security_ID | ||
| - SourceName | ||
| - Subject_Account_Domain | ||
| - Subject_Account_Name | ||
| - Subject_Logon_ID | ||
| - Subject_Security_ID | ||
| - Target_Account_Domain | ||
| - Target_Account_Name | ||
| - Target_Security_ID | ||
| - TaskCategory | ||
| - Type | ||
| - action | ||
| - app | ||
| - body | ||
| - category | ||
| - change_type | ||
| - date_hour | ||
| - date_mday | ||
| - date_minute | ||
| - date_month | ||
| - date_second | ||
| - date_wday | ||
| - date_year | ||
| - date_zone | ||
| - dest | ||
| - dest_nt_domain | ||
| - dest_nt_host | ||
| - dvc | ||
| - dvc_nt_host | ||
| - event_id | ||
| - eventtype | ||
| - host | ||
| - id | ||
| - index | ||
| - linecount | ||
| - member_dn | ||
| - member_id | ||
| - member_nt_domain | ||
| - msad_action | ||
| - name | ||
| - object | ||
| - object_attrs | ||
| - object_category | ||
| - object_id | ||
| - product | ||
| - punct | ||
| - result | ||
| - session_id | ||
| - severity | ||
| - severity_id | ||
| - signature | ||
| - signature_id | ||
| - source | ||
| - sourcetype | ||
| - splunk_server | ||
| - src_nt_domain | ||
| - src_user | ||
| - src_user_name | ||
| - status | ||
| - subject | ||
| - ta_windows_action | ||
| - ta_windows_security_CategoryString | ||
| - tag | ||
| - tag::eventtype | ||
| - timeendpos | ||
| - timestartpos | ||
| - user | ||
| - user_group | ||
| - user_name | ||
| - vendor | ||
| - vendor_product | ||
| example_log: 10/09/2020 10:41:29 AM |
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81
detections/application/detect_distributed_password_spray_attempts.yml
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| name: Detect Distributed Password Spray Attempts | ||
| id: b1a82fc8-8a9f-4344-9ec2-bde5c5331b57 | ||
| version: 1 | ||
| date: '2023-11-01' | ||
| author: Dean Luxton | ||
| status: production | ||
| type: Hunting | ||
| data_source: | ||
| - Azure Active Directory Sign-in activity | ||
| 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. | ||
| search: '| tstats `security_content_summariesonly` dc(Authentication.user) AS unique_accounts dc(Authentication.src) as unique_src count(Authentication.user) as total_failures from datamodel=Authentication.Authentication where Authentication.action="failure" by Authentication.action, Authentication.signature_id, sourcetype, _time span=2m | ||
| | `drop_dm_object_name("Authentication")` | ||
| ```fill out time buckets for 0-count events during entire search length``` | ||
| | appendpipe [| timechart limit=0 span=5m count | table _time] | ||
| | fillnull value=0 unique_accounts, unique_src | ||
| ``` remove duplicate & empty time buckets``` | ||
| | sort - total_failures | ||
| | dedup _time | ||
| ``` Create aggregation field & apply to all null events``` | ||
| | eval counter=sourcetype+"__"+signature_id | ||
| | eventstats values(counter) as fnscounter | eval counter=coalesce(counter,fnscounter) | ||
| ``` 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 | ||
| | 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_accounts >= upperBoundsrc), 1, 0) | ||
| | replace "::ffff:*" with * in src | ||
| | where isOutlier=1 | ||
| | foreach * | ||
| [ eval <<FIELD>> = if(<<FIELD>>="null",null(),<<FIELD>>)] | ||
| | table _time, action, unique_src, unique_accounts, total_failures, sourcetype, signature_id | ||
| | sort - total_failures | `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/ | ||
| 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: unique_accounts | ||
| type: User | ||
| role: | ||
| - Victim | ||
| product: | ||
| - Splunk Enterprise | ||
| - Splunk Enterprise Security | ||
| - Splunk Cloud | ||
| risk_score: 49 | ||
| required_fields: | ||
| - Authentication.action | ||
| - Authentication.user | ||
| - Authentication.src | ||
| security_domain: access | ||
| manual_test: The dataset & hardcoded timerange doesn't meet the criteria for this detetion. | ||
| tests: | ||
| - name: True Positive Test | ||
| attack_data: | ||
| - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/azure_ad_distributed_spray/azure_ad_distributed_spray.log | ||
| source: azure:monitor:aad | ||
| sourcetype: azure:monitor:aad |
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