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Use...","HenriqueMC17\u002Fagente-core","HenriqueMC17","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F206460478?v=4","01M6HB63HBBF18AC4H09XTB549","Henrique Monteiro Cardoso","01M6HB63NGSQXWKWCFWW2B20W0",1,"Apache-2.0","https:\u002F\u002Fgithub.com\u002FHenriqueMC17\u002Fagente-core\u002Fblob\u002Fmain\u002Fmodules\u002Fperforming-user-behavior-analytics\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000013","分析与可视化","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48],"performing","analytics","user","behavior","performing user behavior analytics","user-behavior-analytics","insider","de","travel","cm","access","cybersecurity","analysis","t1566","t1078","behavior-analytics","insider-threat","splunk","soc","siem","2026-11-10 10:12:30",false,"---\nname: performing-user-behavior-analytics\ndescription: Performs User and Entity Behavior Analytics (UEBA) to detect anomalous user activities including impossible travel, unusual access patterns, privilege abuse, and insider threats using SIEM-based behavioral baselines and statistical analysis. Use...\ndomain: cybersecurity\nsubdomain: soc-operations\ntags:\n- soc\n- ueba\n- user-behavior\n- insider-threat\n- anomaly-detection\n- splunk\n- baseline\nversion: '1.0'\nauthor: mahipal\nlicense: Apache-2.0\nnist_csf:\n- DE.CM-01\n- DE.AE-02\n- RS.MA-01\n- DE.AE-06\nmitre_attack:\n- T1078\n- T1685.002\n- T1685.005\n- T1566\n- T0816\ncategory: performing-user-behavior-analytics\n---\n# Performing-User-Behavior-Analytics\n\n## Overview\nPerforms User and Entity Behavior Analytics (UEBA) to detect anomalous user activities including impossible travel, unusual access patterns, privilege abuse, and insider threats using SIEM-based behavioral baselines and statistical analysis. Use when SOC teams need to identify compromised accounts or insider threats through deviation from established behavioral norms.\n\n\n# Performing User Behavior Analytics\n\n## When to Use\n\nUse this skill when:\n- SOC teams need to detect compromised accounts through abnormal authentication patterns\n- Insider threat programs require behavioral monitoring beyond rule-based detection\n- Impossible travel or geographic anomalies indicate credential compromise\n- Privileged account monitoring requires baseline deviation detection\n\n**Do not use** as the sole basis for disciplinary action — UEBA findings are indicators requiring investigation, not proof of malicious intent.\n\n## Prerequisites\n\n- SIEM with 30+ days of authentication and access log history for baseline creation\n- VPN, O365, and Active Directory authentication logs normalized to CIM\n- GeoIP database (MaxMind GeoLite2) for location-based anomaly detection\n- Identity enrichment data (department, role, manager, typical work hours)\n- Splunk Enterprise Security with UBA module or equivalent UEBA capability\n\n## Workflow\n\n### Step 1: Build User Authentication Baselines\n\nCreate behavioral baselines from historical data:\n\n```spl\nindex=auth sourcetype IN (\"o365:management:activity\", \"vpn_logs\", \"WinEventLog:Security\")\nearliest=-30d latest=-1d\n| stats dc(src_ip) AS unique_ips,\n        dc(src_country) AS unique_countries,\n        dc(app) AS unique_apps,\n        count AS total_logins,\n        earliest(_time) AS first_login,\n        latest(_time) AS last_login,\n        values(src_country) AS countries,\n        avg(eval(strftime(_time, \"%H\"))) AS avg_login_hour,\n        stdev(eval(strftime(_time, \"%H\"))) AS stdev_login_hour\n  by user\n| eval avg_daily_logins = round(total_logins \u002F 30, 1)\n| eval login_hour_range = round(avg_login_hour, 0).\" +\u002F- \".round(stdev_login_hour, 1).\" hrs\"\n| table user, unique_ips, unique_countries, unique_apps, avg_daily_logins,\n        login_hour_range, countries\n```\n\n### Step 2: Detect Impossible Travel\n\nIdentify logins from geographically distant locations within impossible timeframes:\n\n```spl\nindex=auth sourcetype IN (\"o365:management:activity\", \"vpn_logs\")\naction=success earliest=-24h\n| iplocation src_ip\n| sort user, _time\n| streamstats current=f last(lat) AS prev_lat, last(lon) AS prev_lon,\n              last(_time) AS prev_time, last(City) AS prev_city,\n              last(Country) AS prev_country, last(src_ip) AS prev_ip\n  by user\n| where isnotnull(prev_lat)\n| eval distance_km = round(\n    6371 * acos(\n      cos(pi()\u002F180 * lat) * cos(pi()\u002F180 * prev_lat) *\n      cos(pi()\u002F180 * (lon - prev_lon)) +\n      sin(pi()\u002F180 * lat) * sin(pi()\u002F180 * prev_lat)\n    ), 0)\n| eval time_diff_hours = round((_time - prev_time) \u002F 3600, 2)\n| eval speed_kmh = if(time_diff_hours > 0, round(distance_km \u002F time_diff_hours, 0), 0)\n| where speed_kmh > 900 AND distance_km > 500\n| eval alert = \"IMPOSSIBLE TRAVEL: \".prev_city.\", \".prev_country.\" -> \".City.\", \".Country\n| table _time, user, prev_city, prev_country, City, Country, distance_km,\n        time_diff_hours, speed_kmh, alert\n| sort - speed_kmh\n```\n\n### Step 3: Detect Anomalous Login Timing\n\nIdentify logins outside a user's normal working hours:\n\n```spl\nindex=auth action=success earliest=-7d\n| eval hour = strftime(_time, \"%H\")\n| eval day_of_week = strftime(_time, \"%A\")\n| eval is_weekend = if(day_of_week IN (\"Saturday\", \"Sunday\"), 1, 0)\n| eval is_off_hours = if(hour \u003C 6 OR hour > 22, 1, 0)\n| join user type=left [\n    search index=auth action=success earliest=-60d latest=-7d\n    | eval hour = strftime(_time, \"%H\")\n    | stats avg(hour) AS baseline_avg_hour, stdev(hour) AS baseline_stdev_hour,\n            perc95(hour) AS baseline_latest_hour by user\n  ]\n| where (is_off_hours=1 OR is_weekend=1) AND\n        (hour > baseline_latest_hour + 2 OR hour \u003C baseline_avg_hour - baseline_stdev_hour * 2)\n| stats count, values(hour) AS login_hours, values(day_of_week) AS login_days,\n        values(src_ip) AS source_ips\n  by user, baseline_avg_hour, baseline_latest_hour\n| where count > 0\n| sort - count\n```\n\n### Step 4: Detect Unusual Data Access Patterns\n\nMonitor for abnormal file or database access volumes:\n\n```spl\nindex=file_access OR index=sharepoint earliest=-24h\n| stats sum(bytes) AS total_bytes, dc(file_path) AS unique_files,\n        count AS access_count by user\n| join user type=left [\n    search index=file_access OR index=sharepoint earliest=-30d latest=-1d\n    | stats avg(eval(count)) AS baseline_avg_files,\n            stdev(eval(count)) AS baseline_stdev_files,\n            avg(eval(sum(bytes))) AS baseline_avg_bytes\n      by user\n  ]\n| eval bytes_gb = round(total_bytes \u002F 1073741824, 2)\n| eval z_score_files = round((unique_files - baseline_avg_files) \u002F baseline_stdev_files, 2)\n| where z_score_files > 3 OR bytes_gb > 5\n| eval anomaly_level = case(\n    z_score_files > 5, \"CRITICAL\",\n    z_score_files > 3, \"HIGH\",\n    bytes_gb > 10, \"CRITICAL\",\n    bytes_gb > 5, \"HIGH\",\n    1=1, \"MEDIUM\"\n  )\n| sort - z_score_files\n| table user, unique_files, bytes_gb, baseline_avg_files, z_score_files, anomaly_level\n```\n\n### Step 5: Detect Privilege Abuse Patterns\n\nMonitor privileged account usage anomalies:\n\n```spl\nindex=wineventlog sourcetype=\"WinEventLog:Security\"\n(EventCode=4672 OR EventCode=4624 OR EventCode=4648) earliest=-24h\n| eval is_privileged = if(EventCode=4672, 1, 0)\n| eval is_explicit_cred = if(EventCode=4648, 1, 0)\n| stats sum(is_privileged) AS priv_events,\n        sum(is_explicit_cred) AS explicit_cred_events,\n        dc(ComputerName) AS unique_hosts,\n        values(ComputerName) AS hosts_accessed\n  by TargetUserName, src_ip\n| join TargetUserName type=left [\n    search index=wineventlog EventCode IN (4672, 4624, 4648) earliest=-30d latest=-1d\n    | stats dc(ComputerName) AS baseline_hosts,\n            avg(eval(count)) AS baseline_daily_events by TargetUserName\n  ]\n| where unique_hosts > baseline_hosts * 2 OR priv_events > baseline_daily_events * 3\n| eval risk_score = (unique_hosts \u002F baseline_hosts * 30) + (priv_events \u002F baseline_daily_events * 20)\n| sort - risk_score\n| table TargetUserName, src_ip, unique_hosts, baseline_hosts, priv_events,\n        baseline_daily_events, risk_score, hosts_accessed\n```\n\n### Step 6: Generate Risk Score and Prioritize Investigation\n\nAggregate all UEBA signals into a composite risk score:\n\n```spl\n| inputlookup ueba_impossible_travel.csv\n| append [| inputlookup ueba_off_hours_access.csv]\n| append [| inputlookup ueba_data_access_anomaly.csv]\n| append [| inputlookup ueba_privilege_abuse.csv]\n| stats sum(risk_points) AS total_risk,\n        values(anomaly_type) AS anomaly_types,\n        dc(anomaly_type) AS anomaly_count\n  by user\n| lookup identity_lookup_expanded identity AS user\n  OUTPUT department, managedBy, priority AS user_priority\n| eval final_risk = total_risk * case(\n    user_priority=\"critical\", 2.0,\n    user_priority=\"high\", 1.5,\n    user_priority=\"medium\", 1.0,\n    1=1, 0.8\n  )\n| sort - final_risk\n| head 20\n| table user, department, managedBy, anomaly_types, anomaly_count, total_risk, final_risk\n```\n\n## Key Concepts\n\n| Term | Definition |\n|------|-----------|\n| **UEBA** | User and Entity Behavior Analytics — behavioral analysis detecting anomalies against established baselines |\n| **Impossible Travel** | Login events from geographically distant locations within timeframes making physical travel impossible |\n| **Behavioral Baseline** | Statistical profile of normal user activity patterns built from 30-90 days of historical data |\n| **Z-Score** | Statistical measure of how many standard deviations an observation is from the mean — values > 3 indicate anomalies |\n| **Risk Score** | Composite numerical score aggregating multiple behavioral anomalies weighted by asset criticality |\n| **Peer Group Analysis** | Comparing a user's behavior to others in the same department\u002Frole to identify outliers |\n\n## Tools & Systems\n\n- **Splunk UBA**: Dedicated User Behavior Analytics module integrating with Splunk ES for ML-driven anomaly detection\n- **Microsoft Sentinel UEBA**: Built-in UEBA capability in Azure Sentinel with entity pages and investigation graphs\n- **Exabeam Advanced Analytics**: Standalone UEBA platform with session stitching and automatic timeline creation\n- **Securonix**: Cloud-native SIEM\u002FUEBA with pre-built behavioral models for insider threat detection\n\n## Common Scenarios\n\n- **Compromised Account**: Impossible travel + off-hours login + unusual app access = likely credential compromise\n- **Insider Data Theft**: Employee accessing 10x normal file volume in notice period before departure\n- **Privilege Escalation Abuse**: Admin account used from unusual location accessing systems outside normal scope\n- **Shared Account Detection**: Service account logging in from multiple geographies simultaneously\n- **Dormant Account Reactivation**: Account with no activity for 90+ days suddenly performing privileged operations\n\n## Output Format\n\n```\nUEBA ANOMALY REPORT — Weekly Summary\n━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\nPeriod:       2024-03-11 to 2024-03-17\nUsers Baselined:  2,847\nAnomalies Detected: 23\n\nTOP RISK USERS:\n#  User          Dept       Risk   Anomalies\n1. jsmith        Finance    94.5   Impossible travel (NYC->Moscow, 2h), off-hours access, 15GB download\n2. admin_svc01   IT Ops     82.0   Login from 12 new IPs, 47 hosts accessed (baseline: 8)\n3. mwilson       HR         67.3   Off-hours file access (2AM), 3x normal download volume\n\nINVESTIGATION STATUS:\n  jsmith:      Escalated to Tier 2 — possible account compromise (IR-2024-0445)\n  admin_svc01: Under review — may be new automation deployment (checking with IT Ops)\n  mwilson:     Pending HR context — employee on notice period, monitoring increased\n```\n","# Performing-User-Behavior-Analytics\n\n## Overview\nPerforms User and Entity Behavior Analytics (UEBA) to detect anomalous user activities including impossible travel, unusual access patterns, privilege abuse, and insider threats using SIEM-based behavioral baselines and statistical analysis. Use when SOC teams need to identify compromised accounts or insider threats through deviation from established behavioral norms.\n\n\n# Performing User Behavior Analytics\n\n## When to Use\n\nUse this skill when:\n- SOC teams need to detect compromised accounts through abnormal authentication patterns\n- Insider threat programs require behavioral monitoring beyond rule-based detection\n- Impossible travel or geographic anomalies indicate credential compromise\n- Privileged account monitoring requires baseline deviation detection\n\n**Do not use** as the sole basis for disciplinary action — UEBA findings are indicators requiring investigation, not proof of malicious intent.\n\n## Prerequisites\n\n- SIEM with 30+ days of authentication and access log history for baseline creation\n- VPN, O365, and Active Directory authentication logs normalized to CIM\n- GeoIP database (MaxMind GeoLite2) for location-based anomaly detection\n- Identity enrichment data (department, role, manager, typical work hours)\n- Splunk Enterprise Security with UBA module or equivalent UEBA capability\n\n## Workflow\n\n### Step 1: Build User Authentication Baselines\n\nCreate behavioral baselines from historical data:\n\n```spl\nindex=auth sourcetype IN (\"o365:management:activity\", \"vpn_logs\", \"WinEventLog:Security\")\nearliest=-30d latest=-1d\n| stats dc(src_ip) AS unique_ips,\n        dc(src_country) AS unique_countries,\n        dc(app) AS unique_apps,\n        count AS total_logins,\n        earliest(_time) AS first_login,\n        latest(_time) AS last_login,\n        values(src_country) AS countries,\n        avg(eval(strftime(_time, \"%H\"))) AS avg_login_hour,\n        stdev(eval(strftime(_time, \"%H\"))) AS stdev_login_hour\n  by user\n| eval avg_daily_logins = round(total_logins \u002F 30, 1)\n| eval login_hour_range = round(avg_login_hour, 0).\" +\u002F- \".round(stdev_login_hour, 1).\" hrs\"\n| table user, unique_ips, unique_countries, unique_apps, avg_daily_logins,\n        login_hour_range, countries\n```\n\n### Step 2: Detect Impossible Travel\n\nIdentify logins from geographically distant locations within impossible timeframes:\n\n```spl\nindex=auth sourcetype IN (\"o365:management:activity\", \"vpn_logs\")\naction=success earliest=-24h\n| iplocation src_ip\n| sort user, _time\n| streamstats current=f last(lat) AS prev_lat, last(lon) AS prev_lon,\n              last(_time) AS prev_time, last(City) AS prev_city,\n              last(Country) AS prev_country, last(src_ip) AS prev_ip\n  by user\n| where isnotnull(prev_lat)\n| eval distance_km = round(\n    6371 * acos(\n      cos(pi()\u002F180 * lat) * cos(pi()\u002F180 * prev_lat) *\n      cos(pi()\u002F180 * (lon - prev_lon)) +\n      sin(pi()\u002F180 * lat) * sin(pi()\u002F180 * prev_lat)\n    ), 0)\n| eval time_diff_hours = round((_time - prev_time) \u002F 3600, 2)\n| eval speed_kmh = if(time_diff_hours > 0, round(distance_km \u002F time_diff_hours, 0), 0)\n| where speed_kmh > 900 AND distance_km > 500\n| eval alert = \"IMPOSSIBLE TRAVEL: \".prev_city.\", \".prev_country.\" -> \".City.\", \".Country\n| table _time, user, prev_city, prev_country, City, Country, distance_km,\n        time_diff_hours, speed_kmh, alert\n| sort - speed_kmh\n```\n\n### Step 3: Detect Anomalous Login Timing\n\nIdentify logins outside a user's normal working hours:\n\n```spl\nindex=auth action=success earliest=-7d\n| eval hour = strftime(_time, \"%H\")\n| eval day_of_week = strftime(_time, \"%A\")\n| eval is_weekend = if(day_of_week IN (\"Saturday\", \"Sunday\"), 1, 0)\n| eval is_off_hours = if(hour \u003C 6 OR hour > 22, 1, 0)\n| join user type=left [\n    search index=auth action=success earliest=-60d latest=-7d\n    | eval hour = strftime(_time, \"%H\")\n    | stats avg(hour) AS baseline_avg_hour, stdev(hour) AS baseline_stdev_hour,\n            perc95(hour) AS baseline_latest_hour by user\n  ]\n| where (is_off_hours=1 OR is_weekend=1) AND\n        (hour > baseline_latest_hour + 2 OR hour \u003C baseline_avg_hour - baseline_stdev_hour * 2)\n| stats count, values(hour) AS login_hours, values(day_of_week) AS login_days,\n        values(src_ip) AS source_ips\n  by user, baseline_avg_hour, baseline_latest_hour\n| where count > 0\n| sort - count\n```\n\n### Step 4: Detect Unusual Data Access Patterns\n\nMonitor for abnormal file or database access volumes:\n\n```spl\nindex=file_access OR index=sharepoint earliest=-24h\n| stats sum(bytes) AS total_bytes, dc(file_path) AS unique_files,\n        count AS access_count by user\n| join user type=left [\n    search index=file_access OR index=sharepoint earliest=-30d latest=-1d\n    | stats avg(eval(count)) AS baseline_avg_files,\n            stdev(eval(count)) AS baseline_stdev_files,\n            avg(eval(sum(bytes))) AS baseline_avg_bytes\n      by user\n  ]\n| eval bytes_gb = round(total_bytes \u002F 1073741824, 2)\n| eval z_score_files = round((unique_files - baseline_avg_files) \u002F baseline_stdev_files, 2)\n| where z_score_files > 3 OR bytes_gb > 5\n| eval anomaly_level = case(\n    z_score_files > 5, \"CRITICAL\",\n    z_score_files > 3, \"HIGH\",\n    bytes_gb > 10, \"CRITICAL\",\n    bytes_gb > 5, \"HIGH\",\n    1=1, \"MEDIUM\"\n  )\n| sort - z_score_files\n| table user, unique_files, bytes_gb, baseline_avg_files, z_score_files, anomaly_level\n```\n\n### Step 5: Detect Privilege Abuse Patterns\n\nMonitor privileged account usage anomalies:\n\n```spl\nindex=wineventlog sourcetype=\"WinEventLog:Security\"\n(EventCode=4672 OR EventCode=4624 OR EventCode=4648) earliest=-24h\n| eval is_privileged = if(EventCode=4672, 1, 0)\n| eval is_explicit_cred = if(EventCode=4648, 1, 0)\n| stats sum(is_privileged) AS priv_events,\n        sum(is_explicit_cred) AS explicit_cred_events,\n        dc(ComputerName) AS unique_hosts,\n        values(ComputerName) AS hosts_accessed\n  by TargetUserName, src_ip\n| join TargetUserName type=left [\n    search index=wineventlog EventCode IN (4672, 4624, 4648) earliest=-30d latest=-1d\n    | stats dc(ComputerName) AS baseline_hosts,\n            avg(eval(count)) AS baseline_daily_events by TargetUserName\n  ]\n| where unique_hosts > baseline_hosts * 2 OR priv_events > baseline_daily_events * 3\n| eval risk_score = (unique_hosts \u002F baseline_hosts * 30) + (priv_events \u002F baseline_daily_events * 20)\n| sort - risk_score\n| table TargetUserName, src_ip, unique_hosts, baseline_hosts, priv_events,\n        baseline_daily_events, risk_score, hosts_accessed\n```\n\n### Step 6: Generate Risk Score and Prioritize Investigation\n\nAggregate all UEBA signals into a composite risk score:\n\n```spl\n| inputlookup ueba_impossible_travel.csv\n| append [| inputlookup ueba_off_hours_access.csv]\n| append [| inputlookup ueba_data_access_anomaly.csv]\n| append [| inputlookup ueba_privilege_abuse.csv]\n| stats sum(risk_points) AS total_risk,\n        values(anomaly_type) AS anomaly_types,\n        dc(anomaly_type) AS anomaly_count\n  by user\n| lookup identity_lookup_expanded identity AS user\n  OUTPUT department, managedBy, priority AS user_priority\n| eval final_risk = total_risk * case(\n    user_priority=\"critical\", 2.0,\n    user_priority=\"high\", 1.5,\n    user_priority=\"medium\", 1.0,\n    1=1, 0.8\n  )\n| sort - final_risk\n| head 20\n| table user, department, managedBy, anomaly_types, anomaly_count, total_risk, final_risk\n```\n\n## Key Concepts\n\n| Term | Definition |\n|------|-----------|\n| **UEBA** | User and Entity Behavior Analytics — behavioral analysis detecting anomalies against established baselines |\n| **Impossible Travel** | Login events from geographically distant locations within timeframes making physical travel impossible |\n| **Behavioral Baseline** | Statistical profile of normal user activity patterns built from 30-90 days of historical data |\n| **Z-Score** | Statistical measure of how many standard deviations an observation is from the mean — values > 3 indicate anomalies |\n| **Risk Score** | Composite numerical score aggregating multiple behavioral anomalies weighted by asset criticality |\n| **Peer Group Analysis** | Comparing a user's behavior to others in the same department\u002Frole to identify outliers |\n\n## Tools & Systems\n\n- **Splunk UBA**: Dedicated User Behavior Analytics module integrating with Splunk ES for ML-driven anomaly detection\n- **Microsoft Sentinel UEBA**: Built-in UEBA capability in Azure Sentinel with entity pages and investigation graphs\n- **Exabeam Advanced Analytics**: Standalone UEBA platform with session stitching and automatic timeline creation\n- **Securonix**: Cloud-native SIEM\u002FUEBA with pre-built behavioral models for insider threat detection\n\n## Common Scenarios\n\n- **Compromised Account**: Impossible travel + off-hours login + unusual app access = likely credential compromise\n- **Insider Data Theft**: Employee accessing 10x normal file volume in notice period before departure\n- **Privilege Escalation Abuse**: Admin account used from unusual location accessing systems outside normal scope\n- **Shared Account Detection**: Service account logging in from multiple geographies simultaneously\n- **Dormant Account Reactivation**: Account with no activity for 90+ days suddenly performing privileged operations\n\n## Output Format\n\n```\nUEBA ANOMALY REPORT — Weekly Summary\n━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\nPeriod:       2024-03-11 to 2024-03-17\nUsers Baselined:  2,847\nAnomalies Detected: 23\n\nTOP RISK USERS:\n#  User          Dept       Risk   Anomalies\n1. jsmith        Finance    94.5   Impossible travel (NYC->Moscow, 2h), off-hours access, 15GB download\n2. admin_svc01   IT Ops     82.0   Login from 12 new IPs, 47 hosts accessed (baseline: 8)\n3. mwilson       HR         67.3   Off-hours file access (2AM), 3x normal download volume\n\nINVESTIGATION STATUS:\n  jsmith:      Escalated to Tier 2 — possible account compromise (IR-2024-0445)\n  admin_svc01: Under review — may be new automation deployment (checking with IT Ops)\n  mwilson:     Pending HR context — employee on notice period, monitoring increased\n```\n",{"name":5,"tags":54,"author":59,"domain":40,"license":15,"version":60,"category":5,"nist_csf":61,"subdomain":66,"description":7,"mitre_attack":67},[47,55,56,45,57,46,58],"ueba","user-behavior","anomaly-detection","baseline","mahipal","1.0",[62,63,64,65],"DE.CM-01","DE.AE-02","RS.MA-01","DE.AE-06","soc-operations",[68,69,70,71,72],"T1078","T1685.002","T1685.005","T1566","T0816","main","5eea5b3296f4673b5f481e8bcc1ba76281149f16","modules\u002Fperforming-user-behavior-analytics",0,"https:\u002F\u002Fgithub.com\u002FHenriqueMC17\u002Fagente-core","https:\u002F\u002Fgithub.com\u002FHenriqueMC17","Qualquer tecnologia suficientemente avançada é indistinguível da mágica.","2026-09-28 22:40:38",{"source":8,"skillName":5,"commands":82,"prompt":92,"zipAvailable":93},[83,86,89],{"tool":84,"command":85},"npx","npx skills add HenriqueMC17\u002Fagente-core --skill 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