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Uses the ZAT library to load Zeek logs into Pandas DataFrames, calculates inter-arrival time standard deviation, and flags periodic connections with low jitter. Use when hunting for command-and-control callbacks in network data.","henriquescastilho\u002Fmy-claude","henriquescastilho","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F153296945?v=4","01M6ESH7EXPXTH9CVQ4K16SRG8","Henrique Scheer","01M6ESH7JHEK2PTNNH4HEH787Z",0,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fhenriquescastilho\u002Fmy-claude\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fdetecting-beaconing-patterns-with-zeek\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"detecting beaconing patterns with zeek","with","patterns","beaconing","Zeek","log-analysis","network-security","conn-log-analysis","zat","statistical-analysis","cybersecurity","command-and-control","use","periodic","cm","security-operations","connections","time","dataframes","pandas","2026-11-10 10:12:30",false,"---\nname: detecting-beaconing-patterns-with-zeek\ndescription: 'Performs statistical analysis of Zeek conn.log connection intervals\n  to detect C2 beaconing patterns. Uses the ZAT library to load Zeek logs into Pandas\n  DataFrames, calculates inter-arrival time standard deviation, and flags periodic\n  connections with low jitter. Use when hunting for command-and-control callbacks\n  in network data.\n\n  '\ndomain: cybersecurity\nsubdomain: security-operations\ntags:\n- network-security\n- zeek\n- c2-beaconing\n- conn-log-analysis\n- zat\n- threat-hunting\n- statistical-analysis\nversion: '1.0'\nauthor: mahipal\nlicense: Apache-2.0\nnist_csf:\n- DE.CM-01\n- RS.MA-01\n- GV.OV-01\n- DE.AE-02\nmitre_attack:\n- T1071.001\n- T1071.004\n- T1573\n- T1008\n- T1095\n---\n\n# Detecting Beaconing Patterns with Zeek\n\n\n## When to Use\n\n- When investigating security incidents that require detecting beaconing patterns with zeek\n- When building detection rules or threat hunting queries for this domain\n- When SOC analysts need structured procedures for this analysis type\n- When validating security monitoring coverage for related attack techniques\n\n## Prerequisites\n\n- Familiarity with security operations concepts and tools\n- Access to a test or lab environment for safe execution\n- Python 3.8+ with required dependencies installed\n- Appropriate authorization for any testing activities\n\n## Instructions\n\nLoad Zeek conn.log data using ZAT (Zeek Analysis Tools), group connections by\nsource\u002Fdestination pairs, and compute timing statistics to identify beaconing.\n\n```python\nfrom zat.log_to_dataframe import LogToDataFrame\nimport numpy as np\n\nlog_to_df = LogToDataFrame()\nconn_df = log_to_df.create_dataframe('\u002Fpath\u002Fto\u002Fconn.log')\n\n# Group by src\u002Fdst pair and calculate inter-arrival time\nfor (src, dst), group in conn_df.groupby(['id.orig_h', 'id.resp_h']):\n    times = group['ts'].sort_values()\n    intervals = times.diff().dt.total_seconds().dropna()\n    if len(intervals) > 10:\n        std_dev = np.std(intervals)\n        mean_interval = np.mean(intervals)\n        # Low std_dev relative to mean = likely beaconing\n```\n\nKey analysis steps:\n1. Parse Zeek conn.log into DataFrame with ZAT LogToDataFrame\n2. Group connections by source IP and destination IP pairs\n3. Calculate inter-arrival time intervals between consecutive connections\n4. Compute standard deviation and coefficient of variation\n5. Flag pairs with low coefficient of variation as potential beacons\n\n## Examples\n\n```python\nfrom zat.log_to_dataframe import LogToDataFrame\nlog_to_df = LogToDataFrame()\ndf = log_to_df.create_dataframe('conn.log')\nprint(df[['id.orig_h', 'id.resp_h', 'ts', 'duration']].head())\n```\n","# Detecting Beaconing Patterns with Zeek\n\n\n## When to Use\n\n- When investigating security incidents that require detecting beaconing patterns with zeek\n- When building detection rules or threat hunting queries for this domain\n- When SOC analysts need structured procedures for this analysis type\n- When validating security monitoring coverage for related attack techniques\n\n## Prerequisites\n\n- Familiarity with security operations concepts and tools\n- Access to a test or lab environment for safe execution\n- Python 3.8+ with required dependencies installed\n- Appropriate authorization for any testing activities\n\n## Instructions\n\nLoad Zeek conn.log data using ZAT (Zeek Analysis Tools), group connections by\nsource\u002Fdestination pairs, and compute timing statistics to identify beaconing.\n\n```python\nfrom zat.log_to_dataframe import LogToDataFrame\nimport numpy as np\n\nlog_to_df = LogToDataFrame()\nconn_df = log_to_df.create_dataframe('\u002Fpath\u002Fto\u002Fconn.log')\n\n# Group by src\u002Fdst pair and calculate inter-arrival time\nfor (src, dst), group in conn_df.groupby(['id.orig_h', 'id.resp_h']):\n    times = group['ts'].sort_values()\n    intervals = times.diff().dt.total_seconds().dropna()\n    if len(intervals) > 10:\n        std_dev = np.std(intervals)\n        mean_interval = np.mean(intervals)\n        # Low std_dev relative to mean = likely beaconing\n```\n\nKey analysis steps:\n1. Parse Zeek conn.log into DataFrame with ZAT LogToDataFrame\n2. Group connections by source IP and destination IP pairs\n3. Calculate inter-arrival time intervals between consecutive connections\n4. Compute standard deviation and coefficient of variation\n5. Flag pairs with low coefficient of variation as potential beacons\n\n## Examples\n\n```python\nfrom zat.log_to_dataframe import LogToDataFrame\nlog_to_df = LogToDataFrame()\ndf = log_to_df.create_dataframe('conn.log')\nprint(df[['id.orig_h', 'id.resp_h', 'ts', 'duration']].head())\n```\n",{"name":5,"tags":44,"author":48,"domain":29,"license":15,"version":49,"nist_csf":50,"subdomain":34,"description":55,"mitre_attack":56},[25,45,46,26,27,47,28],"zeek","c2-beaconing","threat-hunting","mahipal","1.0",[51,52,53,54],"DE.CM-01","RS.MA-01","GV.OV-01","DE.AE-02","Performs statistical analysis of Zeek conn.log connection intervals to detect C2 beaconing patterns. Uses the ZAT library to load Zeek logs into Pandas DataFrames, calculates inter-arrival time standard deviation, and flags periodic connections with low jitter. Use when hunting for command-and-control callbacks in network data.\n",[57,58,59,60,61],"T1071.001","T1071.004","T1573","T1008","T1095","main","7925716f065257435fdb12e97f7e52842147d5ad",".claude\u002Fskills\u002Fdetecting-beaconing-patterns-with-zeek","https:\u002F\u002Fgithub.com\u002Fhenriquescastilho\u002Fmy-claude","https:\u002F\u002Fclaude.com\u002Fclaude-code","https:\u002F\u002Fgithub.com\u002Fhenriquescastilho","Software · Applied AI · Quantitative Modeling · Building with Claude.","2026-06-10 12:24:46",{"source":8,"skillName":5,"commands":71,"prompt":81,"zipAvailable":82},[72,75,78],{"tool":73,"command":74},"npx","npx skills add henriquescastilho\u002Fmy-claude --skill detecting-beaconing-patterns-with-zeek",{"tool":76,"command":77},"bunx","bunx skills add henriquescastilho\u002Fmy-claude --skill detecting-beaconing-patterns-with-zeek",{"tool":79,"command":80},"pnpm","pnpm dlx skills add henriquescastilho\u002Fmy-claude --skill detecting-beaconing-patterns-with-zeek","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fhenriquescastilho\u002Fmy-claude\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fdetecting-beaconing-patterns-with-zeek\u002FSKILL.md",true,[84,89,95],{"path":85,"resourceType":85,"resourceTypeDesc":86,"sizeBytes":87,"sourceHtmlUrl":88},"LICENSE","许可证",11255,"https:\u002F\u002Fgithub.com\u002Fhenriquescastilho\u002Fmy-claude\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fdetecting-beaconing-patterns-with-zeek\u002FLICENSE",{"path":90,"resourceType":91,"resourceTypeDesc":92,"sizeBytes":93,"sourceHtmlUrl":94},"references\u002Fapi-reference.md","REFERENCE","参考文档",1872,"https:\u002F\u002Fgithub.com\u002Fhenriquescastilho\u002Fmy-claude\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fdetecting-beaconing-patterns-with-zeek\u002Freferences\u002Fapi-reference.md",{"path":96,"resourceType":97,"resourceTypeDesc":98,"sizeBytes":99,"sourceHtmlUrl":100},"scripts\u002Fagent.py","SCRIPT","脚本",6474,"https:\u002F\u002Fgithub.com\u002Fhenriquescastilho\u002Fmy-claude\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fdetecting-beaconing-patterns-with-zeek\u002Fscripts\u002Fagent.py",[102,144,153,162,184,212],{"skillNo":103,"name":104,"nameZh":6,"description":105,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":106,"licenseFileUrl":6,"sourceHtmlUrl":107,"categories":108,"keywords":125,"publishedAt":39},"01M6F0B6XPQ65AX2X0DWK8KT3E","markitdown","Convert files and office documents to Markdown. 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