[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71F14WA6QPMKTK1ZEYYFC7M":3,"skill-same:01M71F14WA6QPMKTK1ZEYYFC7M":64},{"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":32,"linkOnly":33,"markdownContent":34,"markdownBody":35,"frontMatter":36,"refName":37,"commitSha":38,"skillDirectory":39,"forksCount":14,"repositoryHtmlUrl":40,"repositoryHomepageUrl":6,"ownerHtmlUrl":41,"ownerBlogUrl":6,"ownerDescription":42,"updatedAt":43,"install":44,"resources":57},"01M71F14WA6QPMKTK1ZEYYFC7M","malware-ransomware-indicators-hunt",null,"6. **Score and Correlate** — Apply composite risk scoring across all indicator types 7.","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-ransomware-indicators-hunt\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31],"indicators","malware ransomware indicators hunt","malware","hunt","ransomware","risk","types","cybersecurity","indicator","risk scoring","apply","scoring","across","2026-11-10 10:12:30",false,"---\nname: malware-ransomware-indicators-hunt\ndescription: \"6. **Score and Correlate** — Apply composite risk scoring across all indicator types 7.\"\ndomain: cybersecurity\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## 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## 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,"domain":26,"description":7},"main","a91f1293287ffa9f4f1b8f486cd6e835a1568c84","skills\u002Fmalware-ransomware-indicators-hunt","https:\u002F\u002Fgithub.com\u002FDCx7C5\u002Fai-marketplace","https:\u002F\u002Fgithub.com\u002FDCx7C5","#Python","2026-09-01 11:21:00",{"source":8,"skillName":5,"commands":45,"prompt":55,"zipAvailable":56},[46,49,52],{"tool":47,"command":48},"npx","npx skills add DCx7C5\u002Fai-marketplace --skill malware-ransomware-indicators-hunt",{"tool":50,"command":51},"bunx","bunx skills add DCx7C5\u002Fai-marketplace --skill malware-ransomware-indicators-hunt",{"tool":53,"command":54},"pnpm","pnpm dlx skills add DCx7C5\u002Fai-marketplace --skill malware-ransomware-indicators-hunt","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FDCx7C5\u002Fai-marketplace\u002Fblob\u002Fmain\u002Fskills\u002Fmalware-ransomware-indicators-hunt\u002FSKILL.md",true,[58],{"path":59,"resourceType":60,"resourceTypeDesc":61,"sizeBytes":62,"sourceHtmlUrl":63},"index.json","REFERENCE","参考文档",593,"https:\u002F\u002Fgithub.com\u002FDCx7C5\u002Fai-marketplace\u002Fblob\u002Fmain\u002Fskills\u002Fmalware-ransomware-indicators-hunt\u002Findex.json",[65,90,112,134,156,172],{"skillNo":66,"name":67,"nameZh":6,"description":68,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":69,"categories":70,"keywords":71,"publishedAt":32},"01M71HAPZ01KQ30RKTB0FFSQ05","services-wmi-persistence-detect","Detect WMI event subscription persistence by enumerating ActiveScriptEventConsumer, 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",[],[72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,26,89],"services","detect","services wmi persistence detect","wmi","persistence","de","cm","process","activescript","event","subscriptions","commandline-consumer","repository","subscription","event-subscription","t1546.003","forensics","process-forensics",{"skillNo":91,"name":92,"nameZh":6,"description":93,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":94,"categories":95,"keywords":96,"publishedAt":32},"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",[],[97,72,98,75,99,100,101,26,102,103,104,31,105,106,77,107,108,109,110,111],"exploit","services wmi exec exploit","exec","remote","traffic","Bash","analysis","team","crackmapexec","native","command","lateral movement","read","lateral-movement","Impacket",{"skillNo":113,"name":114,"nameZh":6,"description":115,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":116,"categories":117,"keywords":118,"publishedAt":32},"01M71HAG6MHKNQV8KDZKMT4886","registry-artifact-extract","Extract and analyze Windows Registry hives to uncover user activity, installed software, autostart entries, and evidence of system compromise.","https:\u002F\u002Fgithub.com\u002FDCx7C5\u002Fai-marketplace\u002Fblob\u002Fmain\u002Fskills\u002Fwindows\u002Fregistry\u002Fartifact\u002Fextract\u002FSKILL.md",[],[119,120,121,122,123,124,26,102,125,103,126,127,77,109,128,129,130,131,132,133],"artifact","registry","registry artifact extract","extract","evidence-collection","artifact-analysis","collection","analyze","uncover","software","activity","digital-forensics","windows-registry","regripper","sonnet",{"skillNo":135,"name":136,"nameZh":6,"description":137,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":138,"categories":139,"keywords":140,"publishedAt":32},"01M71HACR4A8XTWTTSXK4124AA","processes-token-impersonate-detect","Detect Windows token impersonation and privilege abuse including SeImpersonatePrivilege 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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",[],[163,164,73,165,143,145,166,78,167,147,26,89,103,168,169,170,171,155,77,79],"thread","processes thread injection detect","injection","memory","process-injection","api","apc-injection","thread-injection","nt-api",{"skillNo":173,"name":174,"nameZh":6,"description":175,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":176,"categories":177,"keywords":178,"publishedAt":32},"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",[],[179,73,143,180,166,78,181,182,26,89,183,184,185,186,187,188,77,79,189,190],"processes hollow technique detect","tree","memory-forensics","process-hollow","peb","runpe","image","pe-header","in memory","with","mismatched","headers"]