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Use when SOC teams need to identify compromised accounts or insider threats through deviation from established behavioral norms.","mukul975\u002FAnthropic-Cybersecurity-Skills","mukul975","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F42860185?v=4","01M6ADEQR8T5M4EEG6C67BQCWF","Mahipal","01M6ADJPMYWG1E5GM1FWBE3128",35884,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fperforming-user-behavior-analytics\u002FSKILL.md",[18,23,26],{"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":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":27,"categoryName":28,"taxonomyType":29,"taxonomyTypeDesc":30},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48],"performing user behavior analytics","analytics","UEBA","user-behavior","statistical analysis","analysis","baseline","t1566","t1078","insider-threat","cybersecurity","anomaly-detection","detection","splunk","soc","teams","siem","2026-11-10 10:12:30",false,"---\nname: performing-user-behavior-analytics\ndescription: 'Performs User and Entity Behavior Analytics (UEBA) to detect anomalous\n  user activities including impossible travel, unusual access patterns, privilege\n  abuse, and insider threats using SIEM-based behavioral baselines and statistical\n  analysis. Use when SOC teams need to identify compromised accounts or insider threats\n  through deviation from established behavioral norms.\n\n  '\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\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## 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":56,"domain":42,"license":15,"version":57,"nist_csf":58,"subdomain":63,"description":64,"mitre_attack":65},[46,55,35,41,43,45,38],"ueba","mahipal","1.0",[59,60,61,62],"DE.CM-01","DE.AE-02","RS.MA-01","DE.AE-06","soc-operations","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 when SOC teams need to identify compromised accounts or insider threats through deviation from established behavioral norms.\n",[66,67,68,69,70],"T1078","T1685.002","T1685.005","T1566","T0816","main","54a798831d2266a3ca61ce68a7acb80b81160d57","skills\u002Fperforming-user-behavior-analytics",4379,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills","https:\u002F\u002Fmahipal.engineer\u002FAnthropic-Cybersecurity-Skills\u002F","https:\u002F\u002Fgithub.com\u002Fmukul975","Mahipal.engineer","🔐 Cybersecurity | 💻 Dev | 📷 Street Photographer\r\n🎓 MSc | 📚 Research & AI Security","2026-08-31 12:32:44",{"source":8,"skillName":5,"commands":82,"prompt":92,"zipAvailable":93},[83,86,89],{"tool":84,"command":85},"npx","npx skills add mukul975\u002FAnthropic-Cybersecurity-Skills --skill performing-user-behavior-analytics",{"tool":87,"command":88},"bunx","bunx skills add mukul975\u002FAnthropic-Cybersecurity-Skills --skill performing-user-behavior-analytics",{"tool":90,"command":91},"pnpm","pnpm dlx skills add mukul975\u002FAnthropic-Cybersecurity-Skills --skill performing-user-behavior-analytics","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fperforming-user-behavior-analytics\u002FSKILL.md",true,[95,101,106,112,118,122,126],{"path":96,"resourceType":97,"resourceTypeDesc":98,"sizeBytes":99,"sourceHtmlUrl":100},"assets\u002Ftemplate.html","ASSET","资产文件",5837,"https:\u002F\u002Fgithub.com\u002Fjleechanorg\u002Fjleechan-skills\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Ftimeline\u002Fassets\u002Ftemplate.html",{"path":102,"resourceType":102,"resourceTypeDesc":103,"sizeBytes":104,"sourceHtmlUrl":105},"LICENSE","许可证",11255,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fperforming-user-behavior-analytics\u002FLICENSE",{"path":107,"resourceType":108,"resourceTypeDesc":109,"sizeBytes":110,"sourceHtmlUrl":111},"references\u002Fapi-reference.md","REFERENCE","参考文档",2044,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fperforming-user-behavior-analytics\u002Freferences\u002Fapi-reference.md",{"path":113,"resourceType":114,"resourceTypeDesc":115,"sizeBytes":116,"sourceHtmlUrl":117},"scripts\u002Fagent.py","SCRIPT","脚本",8541,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fperforming-user-behavior-analytics\u002Fscripts\u002Fagent.py",{"path":119,"resourceType":114,"resourceTypeDesc":115,"sizeBytes":120,"sourceHtmlUrl":121},"scripts\u002Fbuild.py",17958,"https:\u002F\u002Fgithub.com\u002Fjleechanorg\u002Fjleechan-skills\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Ftimeline\u002Fscripts\u002Fbuild.py",{"path":123,"resourceType":114,"resourceTypeDesc":115,"sizeBytes":124,"sourceHtmlUrl":125},"scripts\u002Freview.sh",2167,"https:\u002F\u002Fgithub.com\u002Fjleechanorg\u002Fjleechan-skills\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Ftimeline\u002Fscripts\u002Freview.sh",{"path":127,"resourceType":114,"resourceTypeDesc":115,"sizeBytes":128,"sourceHtmlUrl":129},"scripts\u002Ftest_build.py",3538,"https:\u002F\u002Fgithub.com\u002Fjleechanorg\u002Fjleechan-skills\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Ftimeline\u002Fscripts\u002Ftest_build.py",[131,158,182,208,234,261],{"skillNo":132,"name":133,"nameZh":6,"description":134,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":135,"categories":136,"keywords":138,"publishedAt":49},"01M6AGDM3HJXTKXX15ZZ0ZVCJ3","verifying-build-provenance-with-slsa-sigstore","Verifies artifact signatures and SLSA provenance using Sigstore's cosign (verify, verify-attestation, verify-blob-attestation) and slsa-verifier (verify-artifact), enforcing keyless OIDC builder identity and source repo against SLSA Build levels. Use in CI\u002FCD before deploying artifacts, when consuming third-party attestations, establishing a SLSA Build L3 pipeline, or confirming provenance during incident response or admission control.","https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fverifying-build-provenance-with-slsa-sigstore\u002FSKILL.md",[137],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[139,140,141,142,143,42,144,145,146,147,148,149,150,151,152,153,154,155,156,157],"sigstore","provenance","slsa","verifying build provenance with slsa sigstore","security","incident response","identity","pr","code","artifacts","ci","supply-chain","pipeline","supply-chain-security","t1195","attestation","code-signing","cosign","keyless",{"skillNo":159,"name":160,"nameZh":6,"description":161,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":162,"categories":163,"keywords":165,"publishedAt":49},"01M6AGDGBPWYVZ2F11GFKJ4HZ9","validating-tpm-measured-boot-attestation","Verifies TPM 2.0 measured-boot integrity and remote attestation with tpm2-tools -- reading PCRs (tpm2_pcrread), replaying the boot event log, generating and checking signed quotes (tpm2_quote\u002Ftpm2_checkquote), and sealing secrets to a PCR policy. Use to confirm a system booted trusted firmware\u002Fkernel for Zero Trust device posture, detect boot-chain tampering via PCR drift, or build a golden-value baseline for fleet attestation.","https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fvalidating-tpm-measured-boot-attestation\u002FSKILL.md",[164],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[166,154,167,168,143,169,42,146,170,171,172,173,174,175,176,177,178,179,180,181],"boot","TPM","validating tpm measured boot attestation","policy","verification","ZeRO","firmware","Tools","measured-boot","PCR","integrity","secrets","remote-attestation","tpm2-tools","integrity-verification","trusted-computing",{"skillNo":183,"name":184,"nameZh":6,"description":185,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":186,"categories":187,"keywords":192,"publishedAt":49},"01M6AGDCW0Y4FXZ3362GT1941M","validating-backup-integrity-for-recovery","Validates backup integrity through cryptographic hash verification, automated restore testing, corruption detection, and recoverability checks to confirm backups are reliable for disaster recovery and ransomware response. Use before relying on backups for recovery, when building post-backup validation pipelines, auditing backup infrastructure for compliance, or checking immutable\u002Fair-gapped backups for silent corruption or tampering.","https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fvalidating-backup-integrity-for-recovery\u002FSKILL.md",[188,191],{"categoryNo":189,"categoryName":190,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[176,193,194,195,196,42,197,198,170,199,200,201,202,203,44,40,204,205,206,207],"backup","recovery","validating backup integrity for recovery","testing","infrastructure","validation","compliance","hash-verification","incident-response","ransomware","cryptocurrency","t1490","immutable","disaster-recovery","restore-testing",{"skillNo":209,"name":210,"nameZh":6,"description":211,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":212,"categories":213,"keywords":215,"publishedAt":49},"01M6AGD9GBG2KZ336AE763J3WH","triaging-windows-with-kape","Runs KAPE (Kroll Artifact Parser and Extractor) to collect targeted forensic artifacts (registry hives, $MFT, event logs, prefetch, browser data) via Targets and parse them with Modules wrapping Eric Zimmerman's EZ Tools (PECmd, MFTECmd, RECmd). Use during early incident containment\u002Ftriage when full disk imaging is impractical but a defensible, parseable Windows artifact set is needed quickly, including at-scale remote collection.","https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Ftriaging-windows-with-kape\u002FSKILL.md",[214],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[216,217,218,42,219,220,221,222,223,224,225,226,227,228,229,230,173,231,232,233],"windows","kape","triaging windows with kape","registry","browser","logs","windows-forensics","dfir","mft","mftecmd","data","prefetch","eric-zimmerman","digital-forensics","SET","collection","pecmd","artifact-collection",{"skillNo":235,"name":236,"nameZh":6,"description":237,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":238,"categories":239,"keywords":241,"publishedAt":49},"01M6AGD5PW5J4EVZM958WD74ZQ","triaging-vulnerabilities-with-ssvc-framework","Triages and prioritizes vulnerabilities with CISA's Stakeholder-Specific Vulnerability Categorization (SSVC) decision tree, weighing exploitation status (via the CISA KEV catalog and FIRST EPSS API), technical impact, automatability, and mission prevalence to output Track\u002FTrack*\u002FAttend\u002FAct decisions. Use when prioritizing vulnerability scan results (OpenVAS, Nessus, Qualys) for remediation planning beyond raw CVSS scores.","https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Ftriaging-vulnerabilities-with-ssvc-framework\u002FSKILL.md",[240],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[242,243,244,42,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260],"vulnerabilities","framework","triaging vulnerabilities with ssvc framework","status","planning","CISA","vulnerability-management","risk-management","nessus","qualys","openvas","cvss","remediation","risk","vulnerability","api","prioritization","ssvc","decision-tree",{"skillNo":262,"name":263,"nameZh":6,"description":264,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":265,"categories":266,"keywords":268,"publishedAt":49},"01M6AGD24TTQQM4CXG69E3B9TJ","triaging-security-incident","Performs initial triage of security incidents using the NIST SP 800-61r3 and SANS PICERL frameworks, classifying incident type, assigning priority by business impact, and routing to the appropriate response team. Use when a SIEM\u002FEDR alert needs human classification, concurrent alerts must be prioritized, or a user report or threat-intel IOC match requires initial incident categorization.","https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Ftriaging-security-incident\u002FSKILL.md",[267],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[143,269,42,48,270,271,46,272,273,274,275,40,276,204,37,277,278,279,280,281],"triaging security incident","file","ioc","NIST-800-61","NIST","severity","edr","execution","classification","severity-classification","incident-triage","SANS-PICERL","SOC-operations"]