[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6AECYV7PN3ADVD50Z696MYT":3,"skill-same:01M6AECYV7PN3ADVD50Z696MYT":100},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":35,"linkOnly":36,"markdownContent":37,"markdownBody":38,"frontMatter":39,"refName":55,"commitSha":56,"skillDirectory":57,"forksCount":58,"repositoryHtmlUrl":59,"repositoryHomepageUrl":60,"ownerHtmlUrl":61,"ownerBlogUrl":62,"ownerDescription":63,"updatedAt":64,"install":65,"resources":78},"01M6AECYV7PN3ADVD50Z696MYT","detecting-exfiltration-over-dns-with-zeek",null,"Detect DNS-based data exfiltration by analyzing Zeek dns.log for high-entropy subdomains, oversized TXT\u002FNULL records, and anomalous query volume or patterns. Use when investigating suspected DNS tunneling, covert C2 over DNS, or data exfiltration hidden in DNS queries against network traffic captured by Zeek.","mukul975\u002FAnthropic-Cybersecurity-Skills","mukul975","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F42860185?v=4","01M6ADEQR8T5M4EEG6C67BQCWF","Mahipal","01M6ADJPMYWG1E5GM1FWBE3128",36078,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fdetecting-exfiltration-over-dns-with-zeek\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34],"dns","exfiltration","Zeek","detecting exfiltration over dns with zeek","security","cybersecurity","pr","threat-hunting","c2","analyzing","network","data","network-security","tunneling","dns-exfiltration","entropy-analysis","2026-11-10 10:12:30",false,"---\nname: detecting-exfiltration-over-dns-with-zeek\ndescription: Detect DNS-based data exfiltration by analyzing Zeek dns.log for high-entropy\n  subdomains, oversized TXT\u002FNULL records, and anomalous query volume or patterns. Use when\n  investigating suspected DNS tunneling, covert C2 over DNS, or data exfiltration hidden\n  in DNS queries against network traffic captured by Zeek.\ndomain: cybersecurity\nsubdomain: network-security\ntags:\n- dns-exfiltration\n- zeek\n- entropy-analysis\n- threat-hunting\nversion: '1.0'\nauthor: mahipal\nlicense: Apache-2.0\nnist_csf:\n- PR.IR-01\n- DE.CM-01\n- ID.AM-03\n- PR.DS-02\nmitre_attack:\n- T1046\n- T1040\n- T1557\n- T1071\n- T1048\n---\n\n\n# Detecting Exfiltration over DNS with Zeek\n\n## Overview\n\nDNS tunneling and exfiltration is a technique used by attackers to bypass firewalls and DLP controls by encoding stolen data into DNS query subdomains. Legitimate DNS queries have predictable entropy and length patterns, while exfiltration queries contain encoded data with high Shannon entropy, unusually long subdomain labels, and high volumes of unique subdomains per parent domain.\n\nThis skill analyzes Zeek dns.log files (TSV format) to detect exfiltration indicators. The agent computes Shannon entropy for each subdomain component, identifies queries exceeding the 63-character DNS label limit, counts unique subdomains per parent domain, and flags domains that exceed configurable thresholds. These techniques detect tools like dnscat2, iodine, dns2tcp, and custom DNS tunneling implementations.\n\n\n## When to Use\n\n- When investigating security incidents that require detecting exfiltration over dns 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- Python 3.9 or later with math and collections modules (stdlib)\n- Zeek dns.log files in TSV format with standard field headers\n- Network capture data processed by Zeek 5.0+ or later\n- Understanding of DNS protocol structure and query types\n\n## Steps\n\n1. **Parse Zeek dns.log headers**: Read the TSV file, extract the `#fields` header line to identify column positions for `ts`, `id.orig_h`, `query`, `qtype_name`, `rcode_name`, and `answers`.\n\n2. **Extract and decompose queries**: For each DNS query, split the FQDN into subdomain labels and parent domain. Skip queries to known safe domains and internal zones.\n\n3. **Compute Shannon entropy**: Calculate the information entropy of each subdomain label. Legitimate subdomains typically have entropy below 3.5, while encoded\u002Fencrypted data produces entropy above 4.0.\n\n4. **Detect long labels**: Flag DNS labels exceeding 52 characters (approaching the 63-character maximum). Long labels are a strong indicator of data tunneling.\n\n5. **Count unique subdomains per domain**: Track how many distinct subdomains each parent domain receives. Domains with more than 50 unique subdomains within the log window are suspicious.\n\n6. **Identify query volume anomalies**: Calculate queries-per-minute per source IP per domain. Exfiltration tools generate sustained high-volume query streams that differ from normal browsing.\n\n7. **Score and rank domains**: Combine entropy, label length, uniqueness count, and query volume into a composite risk score. Rank domains by score and output the top suspicious domains.\n\n8. **Generate detection report**: Produce a JSON report with flagged domains, their evidence indicators, originating source IPs, and recommended response actions.\n\n## Expected Output\n\n```json\n{\n  \"analysis_summary\": {\n    \"total_queries_analyzed\": 145832,\n    \"unique_domains\": 3421,\n    \"flagged_domains\": 3,\n    \"entropy_threshold\": 3.5\n  },\n  \"flagged_domains\": [\n    {\n      \"domain\": \"data.evil-c2.com\",\n      \"unique_subdomains\": 892,\n      \"avg_entropy\": 4.72,\n      \"max_label_length\": 61,\n      \"source_ips\": [\"10.0.1.45\"],\n      \"risk_score\": 9.4,\n      \"indicators\": [\"high_entropy\", \"long_labels\", \"high_subdomain_count\"]\n    }\n  ]\n}\n```\n","# Detecting Exfiltration over DNS with Zeek\n\n## Overview\n\nDNS tunneling and exfiltration is a technique used by attackers to bypass firewalls and DLP controls by encoding stolen data into DNS query subdomains. Legitimate DNS queries have predictable entropy and length patterns, while exfiltration queries contain encoded data with high Shannon entropy, unusually long subdomain labels, and high volumes of unique subdomains per parent domain.\n\nThis skill analyzes Zeek dns.log files (TSV format) to detect exfiltration indicators. The agent computes Shannon entropy for each subdomain component, identifies queries exceeding the 63-character DNS label limit, counts unique subdomains per parent domain, and flags domains that exceed configurable thresholds. These techniques detect tools like dnscat2, iodine, dns2tcp, and custom DNS tunneling implementations.\n\n\n## When to Use\n\n- When investigating security incidents that require detecting exfiltration over dns 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- Python 3.9 or later with math and collections modules (stdlib)\n- Zeek dns.log files in TSV format with standard field headers\n- Network capture data processed by Zeek 5.0+ or later\n- Understanding of DNS protocol structure and query types\n\n## Steps\n\n1. **Parse Zeek dns.log headers**: Read the TSV file, extract the `#fields` header line to identify column positions for `ts`, `id.orig_h`, `query`, `qtype_name`, `rcode_name`, and `answers`.\n\n2. **Extract and decompose queries**: For each DNS query, split the FQDN into subdomain labels and parent domain. Skip queries to known safe domains and internal zones.\n\n3. **Compute Shannon entropy**: Calculate the information entropy of each subdomain label. Legitimate subdomains typically have entropy below 3.5, while encoded\u002Fencrypted data produces entropy above 4.0.\n\n4. **Detect long labels**: Flag DNS labels exceeding 52 characters (approaching the 63-character maximum). Long labels are a strong indicator of data tunneling.\n\n5. **Count unique subdomains per domain**: Track how many distinct subdomains each parent domain receives. Domains with more than 50 unique subdomains within the log window are suspicious.\n\n6. **Identify query volume anomalies**: Calculate queries-per-minute per source IP per domain. Exfiltration tools generate sustained high-volume query streams that differ from normal browsing.\n\n7. **Score and rank domains**: Combine entropy, label length, uniqueness count, and query volume into a composite risk score. Rank domains by score and output the top suspicious domains.\n\n8. **Generate detection report**: Produce a JSON report with flagged domains, their evidence indicators, originating source IPs, and recommended response actions.\n\n## Expected Output\n\n```json\n{\n  \"analysis_summary\": {\n    \"total_queries_analyzed\": 145832,\n    \"unique_domains\": 3421,\n    \"flagged_domains\": 3,\n    \"entropy_threshold\": 3.5\n  },\n  \"flagged_domains\": [\n    {\n      \"domain\": \"data.evil-c2.com\",\n      \"unique_subdomains\": 892,\n      \"avg_entropy\": 4.72,\n      \"max_label_length\": 61,\n      \"source_ips\": [\"10.0.1.45\"],\n      \"risk_score\": 9.4,\n      \"indicators\": [\"high_entropy\", \"long_labels\", \"high_subdomain_count\"]\n    }\n  ]\n}\n```\n",{"name":5,"tags":40,"author":42,"domain":24,"license":15,"version":43,"nist_csf":44,"subdomain":31,"description":7,"mitre_attack":49},[33,41,34,26],"zeek","mahipal","1.0",[45,46,47,48],"PR.IR-01","DE.CM-01","ID.AM-03","PR.DS-02",[50,51,52,53,54],"T1046","T1040","T1557","T1071","T1048","main","54a798831d2266a3ca61ce68a7acb80b81160d57","skills\u002Fdetecting-exfiltration-over-dns-with-zeek",4414,"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":66,"prompt":76,"zipAvailable":77},[67,70,73],{"tool":68,"command":69},"npx","npx skills add mukul975\u002FAnthropic-Cybersecurity-Skills --skill detecting-exfiltration-over-dns-with-zeek",{"tool":71,"command":72},"bunx","bunx skills add mukul975\u002FAnthropic-Cybersecurity-Skills --skill detecting-exfiltration-over-dns-with-zeek",{"tool":74,"command":75},"pnpm","pnpm dlx skills add mukul975\u002FAnthropic-Cybersecurity-Skills --skill detecting-exfiltration-over-dns-with-zeek","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fdetecting-exfiltration-over-dns-with-zeek\u002FSKILL.md",true,[79,85,90,94],{"path":80,"resourceType":81,"resourceTypeDesc":82,"sizeBytes":83,"sourceHtmlUrl":84},"agents\u002Fopenai.yaml","REFERENCE","参考文档",43,"https:\u002F\u002Fgithub.com\u002FEvanston09\u002Fdotfiles\u002Fblob\u002Fmain\u002Fagents\u002Fthermo-nuclear-code-quality-review\u002Fagents\u002Fopenai.yaml",{"path":86,"resourceType":86,"resourceTypeDesc":87,"sizeBytes":88,"sourceHtmlUrl":89},"LICENSE","许可证",11255,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fdetecting-exfiltration-over-dns-with-zeek\u002FLICENSE",{"path":91,"resourceType":81,"resourceTypeDesc":82,"sizeBytes":92,"sourceHtmlUrl":93},"references\u002Fapi-reference.md",2639,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fdetecting-exfiltration-over-dns-with-zeek\u002Freferences\u002Fapi-reference.md",{"path":95,"resourceType":96,"resourceTypeDesc":97,"sizeBytes":98,"sourceHtmlUrl":99},"scripts\u002Fagent.py","SCRIPT","脚本",7515,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fdetecting-exfiltration-over-dns-with-zeek\u002Fscripts\u002Fagent.py",[101,130,154,182,207,234],{"skillNo":102,"name":103,"nameZh":6,"description":104,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":105,"categories":106,"keywords":112,"publishedAt":35},"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",[107],{"categoryNo":108,"categoryName":109,"taxonomyType":110,"taxonomyTypeDesc":111},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[113,114,115,116,23,24,117,118,25,119,120,121,122,123,124,125,126,127,128,129],"sigstore","provenance","slsa","verifying build provenance with slsa sigstore","incident response","identity","code","artifacts","ci","supply-chain","pipeline","supply-chain-security","t1195","attestation","code-signing","cosign","keyless",{"skillNo":131,"name":132,"nameZh":6,"description":133,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":134,"categories":135,"keywords":137,"publishedAt":35},"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",[136],{"categoryNo":108,"categoryName":109,"taxonomyType":110,"taxonomyTypeDesc":111},[138,126,139,140,23,141,24,25,142,143,144,145,146,147,148,149,150,151,152,153],"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":155,"name":156,"nameZh":6,"description":157,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":158,"categories":159,"keywords":164,"publishedAt":35},"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",[160,163],{"categoryNo":161,"categoryName":162,"taxonomyType":110,"taxonomyTypeDesc":111},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":108,"categoryName":109,"taxonomyType":110,"taxonomyTypeDesc":111},[148,165,166,167,168,24,169,170,142,171,172,173,174,175,176,177,178,179,180,181],"backup","recovery","validating backup integrity for recovery","testing","infrastructure","validation","compliance","hash-verification","incident-response","ransomware","cryptocurrency","detection","t1078","t1490","immutable","disaster-recovery","restore-testing",{"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":189,"publishedAt":35},"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",[188],{"categoryNo":108,"categoryName":109,"taxonomyType":110,"taxonomyTypeDesc":111},[190,191,192,24,193,194,195,196,197,198,199,30,200,201,202,203,145,204,205,206],"windows","kape","triaging windows with kape","registry","browser","logs","windows-forensics","dfir","mft","mftecmd","prefetch","eric-zimmerman","digital-forensics","SET","collection","pecmd","artifact-collection",{"skillNo":208,"name":209,"nameZh":6,"description":210,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":211,"categories":212,"keywords":214,"publishedAt":35},"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",[213],{"categoryNo":108,"categoryName":109,"taxonomyType":110,"taxonomyTypeDesc":111},[215,216,217,24,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233],"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":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":35},"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",[240],{"categoryNo":108,"categoryName":109,"taxonomyType":110,"taxonomyTypeDesc":111},[23,242,24,243,244,245,246,247,248,249,250,177,251,178,252,253,254,255,256,257],"triaging security incident","siem","file","ioc","soc","NIST-800-61","NIST","severity","edr","execution","analysis","classification","severity-classification","incident-triage","SANS-PICERL","SOC-operations"]