[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71F7A8RJC71D7V2J4476XE5":3,"skill-same:01M71F7A8RJC71D7V2J4476XE5":99},{"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":39,"linkOnly":40,"markdownContent":41,"markdownBody":42,"frontMatter":43,"refName":72,"commitSha":73,"skillDirectory":74,"forksCount":14,"repositoryHtmlUrl":75,"repositoryHomepageUrl":6,"ownerHtmlUrl":76,"ownerBlogUrl":6,"ownerDescription":77,"updatedAt":78,"install":79,"resources":92},"01M71F7A8RJC71D7V2J4476XE5","ransomware-indicators-hunt",null,"Identify ransomware network indicators including C2 beaconing patterns, TOR exit node connections, data exfiltration flows, and encryption key exchange via Zeek conn.log and NetFlow analysis","DCx7C5\u002Fai-marketplace","DCx7C5","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F42419559?v=4","01M71C01G2AZV3NTNGWAWSHJPP","DC","01M71C01GQW6G50S2BYMS7PP3E",0,"MIT","https:\u002F\u002Fgithub.com\u002FDCx7C5\u002Fai-marketplace\u002Fblob\u002Fmain\u002Fskills\u002Fmalware\u002Fransomware\u002Findicators\u002Fhunt\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"ransomware indicators hunt","ransomware","indicators","hunt","command","content analysis","hunting","flows","de","conversion","log","cm","cybersecurity","Bash","analysis","beaconing","c2-beaconing","encryption","network-forensics","exfiltration","2026-11-10 10:12:30",false,"---\nname: ransomware-indicators-hunt\ndescription: Identify ransomware network indicators including C2 beaconing patterns, TOR exit node connections, data exfiltration flows, and encryption key exchange via Zeek conn.log and NetFlow analysis\ndomain: cybersecurity\nsubdomain: threat-hunting\ntags:\n- ransomware\n- c2-beaconing\n- zeek\n- netflow\n- tor\n- exfiltration\n- network-forensics\nd3fend_techniques:\n- File Metadata Consistency Validation\n- Certificate Analysis\n- Application Protocol Command Analysis\n- Content Format Conversion\n- File Content Analysis\nnist_csf:\n- DE.CM-01\n- DE.AE-02\n- DE.AE-07\n- ID.RA-05\nmodel: sonnet\nmaxTurns: 20\ntools: [Read, Bash, Glob, Grep]\nmitre_attack:\n- T1041\n- T1071\n- T1071.001\n- T1486\ncapec: []\n---\n\n# Analyzing Ransomware Network Indicators\n\n## Overview\n\nBefore and during ransomware execution, adversaries establish C2 channels, exfiltrate data, and download encryption keys. This skill analyzes Zeek conn.log and NetFlow data to detect beaconing patterns (regular-interval callbacks), connections to known TOR exit nodes, large outbound data transfers, and suspicious DNS activity associated with ransomware families.\n\n\n## When to Use\n\n- When investigating security incidents that require analyzing ransomware network indicators\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- Zeek conn.log files or NetFlow CSV\u002FJSON exports\n- Python 3.8+ with standard library\n- TOR exit node list (fetched from Tor Project or threat intel feeds)\n- Optional: Known ransomware C2 IOC list\n\n## Steps\n\n1. **Parse Connection Logs** — Ingest Zeek conn.log (TSV) or NetFlow records into structured format\n2. **Detect Beaconing Patterns** — Calculate connection interval statistics (mean, stddev, coefficient of variation) to identify periodic callbacks\n3. **Check TOR Exit Node Connections** — Cross-reference destination IPs against current TOR exit node list\n4. **Identify Data Exfiltration** — Flag connections with unusually high outbound byte ratios to external IPs\n5. **Analyze DNS Patterns** — Detect DGA-like domain queries and high-entropy subdomains\n6. **Score and Correlate** — Apply composite risk scoring across all indicator types\n7. **Generate Report** — Produce structured report with timeline and MITRE ATT&CK mapping\n\n## Expected Output\n\n- JSON report with beaconing detections and interval statistics\n- TOR exit node connection alerts\n- Data exfiltration flow analysis\n- Composite ransomware risk score with MITRE mapping (T1071, T1573, T1041)\n","# Analyzing Ransomware Network Indicators\n\n## Overview\n\nBefore and during ransomware execution, adversaries establish C2 channels, exfiltrate data, and download encryption keys. This skill analyzes Zeek conn.log and NetFlow data to detect beaconing patterns (regular-interval callbacks), connections to known TOR exit nodes, large outbound data transfers, and suspicious DNS activity associated with ransomware families.\n\n\n## When to Use\n\n- When investigating security incidents that require analyzing ransomware network indicators\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- Zeek conn.log files or NetFlow CSV\u002FJSON exports\n- Python 3.8+ with standard library\n- TOR exit node list (fetched from Tor Project or threat intel feeds)\n- Optional: Known ransomware C2 IOC list\n\n## Steps\n\n1. **Parse Connection Logs** — Ingest Zeek conn.log (TSV) or NetFlow records into structured format\n2. **Detect Beaconing Patterns** — Calculate connection interval statistics (mean, stddev, coefficient of variation) to identify periodic callbacks\n3. **Check TOR Exit Node Connections** — Cross-reference destination IPs against current TOR exit node list\n4. **Identify Data Exfiltration** — Flag connections with unusually high outbound byte ratios to external IPs\n5. **Analyze DNS Patterns** — Detect DGA-like domain queries and high-entropy subdomains\n6. **Score and Correlate** — Apply composite risk scoring across all indicator types\n7. **Generate Report** — Produce structured report with timeline and MITRE ATT&CK mapping\n\n## Expected Output\n\n- JSON report with beaconing detections and interval statistics\n- TOR exit node connection alerts\n- Data exfiltration flow analysis\n- Composite ransomware risk score with MITRE mapping (T1071, T1573, T1041)\n",{"name":5,"tags":44,"capec":48,"model":49,"tools":50,"domain":31,"maxTurns":54,"nist_csf":55,"subdomain":60,"description":7,"mitre_attack":61,"d3fend_techniques":66},[20,35,45,46,47,38,37],"zeek","netflow","tor",[],"sonnet",[51,32,52,53],"Read","Glob","Grep",20,[56,57,58,59],"DE.CM-01","DE.AE-02","DE.AE-07","ID.RA-05","threat-hunting",[62,63,64,65],"T1041","T1071","T1071.001","T1486",[67,68,69,70,71],"File Metadata Consistency Validation","Certificate Analysis","Application Protocol Command Analysis","Content Format Conversion","File Content Analysis","main","a91f1293287ffa9f4f1b8f486cd6e835a1568c84","skills\u002Fmalware\u002Fransomware\u002Findicators\u002Fhunt","https:\u002F\u002Fgithub.com\u002FDCx7C5\u002Fai-marketplace","https:\u002F\u002Fgithub.com\u002FDCx7C5","#Python","2026-09-01 11:21:00",{"source":8,"skillName":5,"commands":80,"prompt":90,"zipAvailable":91},[81,84,87],{"tool":82,"command":83},"npx","npx skills add DCx7C5\u002Fai-marketplace --skill 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CommandLineEventConsumer, and permanent subscriptions in the WMI repository.","https:\u002F\u002Fgithub.com\u002FDCx7C5\u002Fai-marketplace\u002Fblob\u002Fmain\u002Fskills\u002Fwindows\u002Fservices\u002Fwmi\u002Fpersistence\u002Fdetect\u002FSKILL.md",[],[107,108,109,110,111,27,30,112,113,114,115,116,117,118,119,120,121,31,122],"services","detect","services wmi persistence detect","wmi","persistence","process","activescript","event","subscriptions","commandline-consumer","repository","subscription","event-subscription","t1546.003","forensics","process-forensics",{"skillNo":124,"name":125,"nameZh":6,"description":126,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":127,"categories":128,"keywords":129,"publishedAt":39},"01M71HAK6JTTTS229T17WJMMCG","services-wmi-exec-exploit","Perform lateral movement across Windows networks using WMI-based remote execution techniques including Impacket wmiexec.py, CrackMapExec, and native WMI commands for stealthy post-exploitation during red team engagements.","https:\u002F\u002Fgithub.com\u002FDCx7C5\u002Fai-marketplace\u002Fblob\u002Fmain\u002Fskills\u002Fwindows\u002Fservices\u002Fwmi\u002Fexec\u002Fexploit\u002FSKILL.md",[],[130,107,131,110,132,133,134,31,32,33,135,136,137,138,27,23,139,140,141,142],"exploit","services wmi exec exploit","exec","remote","traffic","team","across","crackmapexec","native","lateral movement","read","lateral-movement","Impacket",{"skillNo":144,"name":145,"nameZh":6,"description":146,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":147,"categories":148,"keywords":149,"publishedAt":39},"01M71HAG6MHKNQV8KDZKMT4886","registry-artifact-extract","Extract and analyze Windows Registry hives to 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including CreateRemoteThread, QueueUserAPC, NtCreateThreadEx, and thread hijacking via process memory analysis.","https:\u002F\u002Fgithub.com\u002FDCx7C5\u002Fai-marketplace\u002Fblob\u002Fmain\u002Fskills\u002Fwindows\u002Fprocesses\u002Fthread\u002Finjection\u002Fdetect\u002FSKILL.md",[],[193,194,108,195,173,175,196,30,197,177,31,122,33,198,199,200,201,185,27,112],"thread","processes thread injection detect","injection","memory","process-injection","api","apc-injection","thread-injection","nt-api",{"skillNo":203,"name":204,"nameZh":6,"description":205,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":206,"categories":207,"keywords":208,"publishedAt":39},"01M71HA7H311V5G14WMX58HQSS","processes-hollow-technique-detect","Detect process hollowing (RunPE) by identifying processes with mismatched image paths between PEB and VAD tree, or unmapped PE headers in memory.","https:\u002F\u002Fgithub.com\u002FDCx7C5\u002Fai-marketplace\u002Fblob\u002Fmain\u002Fskills\u002Fwindows\u002Fprocesses\u002Fhollow\u002Ftechnique\u002Fdetect\u002FSKILL.md",[],[209,108,173,210,196,30,211,212,31,122,213,214,215,216,217,218,27,112,219,220],"processes hollow technique detect","tree","memory-forensics","process-hollow","peb","runpe","image","pe-header","in memory","with","mismatched","headers"]