[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71FYB2RA7ZPZN61ZTE6403M":3,"skill-same:01M71FYB2RA7ZPZN61ZTE6403M":57},{"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":25,"linkOnly":26,"markdownContent":27,"markdownBody":28,"frontMatter":29,"refName":30,"commitSha":31,"skillDirectory":32,"forksCount":14,"repositoryHtmlUrl":33,"repositoryHomepageUrl":6,"ownerHtmlUrl":34,"ownerBlogUrl":6,"ownerDescription":35,"updatedAt":36,"install":37,"resources":50},"01M71FYB2RA7ZPZN61ZTE6403M","soc-hunting-advanced-hunt",null,"Soc Hunting Advanced Hunt.","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\u002Fsoc-hunting-advanced-hunt\u002FSKILL.md",[],[19,20,21,22,23,24],"soc hunting advanced hunt","hunt","hunting","advanced","soc","cybersecurity","2026-11-10 10:12:30",false,"---\nname: soc-hunting-advanced-hunt\ndescription: \"Soc Hunting Advanced Hunt.\"\ndomain: cybersecurity\n---\n\n--|\n| **TTP** | Tactics, Techniques, and Procedures — adversary behavioral patterns as defined in MITRE ATT&CK |\n| **Diamond Model** | Analytical framework with four vertices (adversary, capability, infrastructure, victim) used to structure intrusion analysis |\n| **Living-off-the-Land (LotL)** | Attacker technique using legitimate OS tools (PowerShell, WMI, certutil) to evade detection |\n| **UEBA** | User and Entity Behavior Analytics — ML-based detection of anomalous behavior baselines |\n| **Sigma** | Open standard for SIEM-agnostic detection rule format, analogous to YARA for network\u002Flog detection |\n| **Hunt Hypothesis** | A testable prediction about adversary presence based on threat intelligence and environmental knowledge |\n\n## Tools & Systems\n\n- **Velociraptor**: Open-source DFIR platform with VQL query language for scalable endpoint hunting across thousands of systems\n- **osquery**: SQL-based OS instrumentation framework for real-time endpoint telemetry queries\n- **MITRE ATT&CK Navigator**: Web-based tool for visualizing ATT&CK coverage and technique prioritization\n- **Zeek (formerly Bro)**: Network traffic analyzer producing structured logs (conn, dns, http, ssl) suitable for hunting\n- **Elastic Security**: EQL (Event Query Language) enables sequence-based hunting for multi-stage attack patterns\n- **Sigma**: Detection rule format with translators for Splunk, QRadar, Sentinel, and Elastic\n\n## Common Pitfalls\n\n- **Confirmation bias**: Starting a hunt expecting to find something and interpreting benign data as malicious. Document null results — they validate controls.\n- **Insufficient data retention**: Many APT techniques require 90+ days of log history to identify slow-and-low patterns. Default retention periods are often too short.\n- **Hunting without baselines**: Cannot identify anomalies without knowing normal. Spend time on baseline documentation before hunting.\n- **Query performance impact**: Broad queries against production SIEM during business hours can degrade analyst workflows. Schedule intensive hunts during off-peak hours.\n- **Ignoring false positives systematically**: Track false positive rates per query. Queries with >80% FP rate should be refined or retired before operationalization.\n","--|\n| **TTP** | Tactics, Techniques, and Procedures — adversary behavioral patterns as defined in MITRE ATT&CK |\n| **Diamond Model** | Analytical framework with four vertices (adversary, capability, infrastructure, victim) used to structure intrusion analysis |\n| **Living-off-the-Land (LotL)** | Attacker technique using legitimate OS tools (PowerShell, WMI, certutil) to evade detection |\n| **UEBA** | User and Entity Behavior Analytics — ML-based detection of anomalous behavior baselines |\n| **Sigma** | Open standard for SIEM-agnostic detection rule format, analogous to YARA for network\u002Flog detection |\n| **Hunt Hypothesis** | A testable prediction about adversary presence based on threat intelligence and environmental knowledge |\n\n## Tools & Systems\n\n- **Velociraptor**: Open-source DFIR platform with VQL query language for scalable endpoint hunting across thousands of systems\n- **osquery**: SQL-based OS instrumentation framework for real-time endpoint telemetry queries\n- **MITRE ATT&CK Navigator**: Web-based tool for visualizing ATT&CK coverage and technique prioritization\n- **Zeek (formerly Bro)**: Network traffic analyzer producing structured logs (conn, dns, http, ssl) suitable for hunting\n- **Elastic Security**: EQL (Event Query Language) enables sequence-based hunting for multi-stage attack patterns\n- **Sigma**: Detection rule format with translators for Splunk, QRadar, Sentinel, and Elastic\n\n## Common Pitfalls\n\n- **Confirmation bias**: Starting a hunt expecting to find something and interpreting benign data as malicious. Document null results — they validate controls.\n- **Insufficient data retention**: Many APT techniques require 90+ days of log history to identify slow-and-low patterns. Default retention periods are often too short.\n- **Hunting without baselines**: Cannot identify anomalies without knowing normal. Spend time on baseline documentation before hunting.\n- **Query performance impact**: Broad queries against production SIEM during business hours can degrade analyst workflows. Schedule intensive hunts during off-peak hours.\n- **Ignoring false positives systematically**: Track false positive rates per query. Queries with >80% FP rate should be refined or retired before operationalization.\n",{"name":5,"domain":24,"description":7},"main","a91f1293287ffa9f4f1b8f486cd6e835a1568c84","skills\u002Fsoc-hunting-advanced-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":38,"prompt":48,"zipAvailable":49},[39,42,45],{"tool":40,"command":41},"npx","npx skills add DCx7C5\u002Fai-marketplace --skill soc-hunting-advanced-hunt",{"tool":43,"command":44},"bunx","bunx skills add DCx7C5\u002Fai-marketplace --skill soc-hunting-advanced-hunt",{"tool":46,"command":47},"pnpm","pnpm dlx skills add DCx7C5\u002Fai-marketplace --skill soc-hunting-advanced-hunt","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FDCx7C5\u002Fai-marketplace\u002Fblob\u002Fmain\u002Fskills\u002Fsoc-hunting-advanced-hunt\u002FSKILL.md",true,[51],{"path":52,"resourceType":53,"resourceTypeDesc":54,"sizeBytes":55,"sourceHtmlUrl":56},"index.json","REFERENCE","参考文档",500,"https:\u002F\u002Fgithub.com\u002FDCx7C5\u002Fai-marketplace\u002Fblob\u002Fmain\u002Fskills\u002Fsoc-hunting-advanced-hunt\u002Findex.json",[58,83,106,128,150,166],{"skillNo":59,"name":60,"nameZh":6,"description":61,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":62,"categories":63,"keywords":64,"publishedAt":25},"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",[],[65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,24,82],"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":84,"name":85,"nameZh":6,"description":86,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":87,"categories":88,"keywords":89,"publishedAt":25},"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",[],[90,65,91,68,92,93,94,24,95,96,97,98,99,100,70,101,102,103,104,105],"exploit","services wmi exec exploit","exec","remote","traffic","Bash","analysis","team","across","crackmapexec","native","command","lateral movement","read","lateral-movement","Impacket",{"skillNo":107,"name":108,"nameZh":6,"description":109,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":110,"categories":111,"keywords":112,"publishedAt":25},"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",[],[113,114,115,116,117,118,24,95,119,96,120,121,70,103,122,123,124,125,126,127],"artifact","registry","registry artifact extract","extract","evidence-collection","artifact-analysis","collection","analyze","uncover","software","activity","digital-forensics","windows-registry","regripper","sonnet",{"skillNo":129,"name":130,"nameZh":6,"description":131,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":132,"categories":133,"keywords":134,"publishedAt":25},"01M71HACR4A8XTWTTSXK4124AA","processes-token-impersonate-detect","Detect Windows token impersonation and privilege abuse including SeImpersonatePrivilege exploitation via Potato attacks and token stealing techniques.","https:\u002F\u002Fgithub.com\u002FDCx7C5\u002Fai-marketplace\u002Fblob\u002Fmain\u002Fskills\u002Fwindows\u002Fprocesses\u002Ftoken\u002Fimpersonate\u002Fdetect\u002FSKILL.md",[],[66,135,136,137,138,139,140,71,141,24,82,142,143,144,145,146,147,148,149,70],"processes token impersonate detect","token","processes","attacks","windows","escalation","techniques","privilege-escalation","seimpersonate","token-impersonation","printspoofer","exploitation","potato","impersonation","via",{"skillNo":151,"name":152,"nameZh":6,"description":153,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":154,"categories":155,"keywords":156,"publishedAt":25},"01M71HAABSB4Z6QFSXEG00R4RK","processes-thread-injection-detect","Detect Windows thread injection techniques 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",[],[157,158,66,159,137,139,160,71,161,141,24,82,96,162,163,164,165,149,70,72],"thread","processes thread injection detect","injection","memory","process-injection","api","apc-injection","thread-injection","nt-api",{"skillNo":167,"name":168,"nameZh":6,"description":169,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":170,"categories":171,"keywords":172,"publishedAt":25},"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",[],[173,66,137,174,160,71,175,176,24,82,177,178,179,180,181,182,70,72,183,184],"processes hollow technique detect","tree","memory-forensics","process-hollow","peb","runpe","image","pe-header","in memory","with","mismatched","headers"]