[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71FGSNY1FESPWDGXWT1M5AD":3,"skill-same:01M71FGSNY1FESPWDGXWT1M5AD":61},{"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":29,"linkOnly":30,"markdownContent":31,"markdownBody":32,"frontMatter":33,"refName":34,"commitSha":35,"skillDirectory":36,"forksCount":14,"repositoryHtmlUrl":37,"repositoryHomepageUrl":6,"ownerHtmlUrl":38,"ownerBlogUrl":6,"ownerDescription":39,"updatedAt":40,"install":41,"resources":54},"01M71FGSNY1FESPWDGXWT1M5AD","net-traffic-net",null,"- [MITRE ATT&CK T1071 - Application Layer Protocol](https:\u002F\u002Fattack.","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\u002Fnet-traffic-net\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28],"traffic","net traffic net","cybersecurity","protocol","https","ATT&CK","application","layer","ck","attack","2026-11-10 10:12:30",false,"---\nname: net-traffic-net\ndescription: \"- [MITRE ATT&CK T1071 - Application Layer Protocol](https:\u002F\u002Fattack.\"\ndomain: cybersecurity\n---\n\n# Analyzing Network Covert Channels in Malware\n\n## Overview\n\nMalware uses covert channels to disguise C2 communication and data exfiltration within legitimate-looking network traffic. DNS tunneling encodes data in DNS queries and responses (used by tools like iodine, dnscat2, and malware families like FrameworkPOS). ICMP tunneling hides data in echo request\u002Freply payloads (icmpsh, ptunnel). HTTP covert channels embed C2 data in headers, cookies, or steganographic images. Protocol abuse exploits allowed protocols to bypass firewalls. DNS tunneling detection achieves 99%+ recall with modern ML-based approaches, though low-throughput exfiltration remains challenging. Palo Alto Unit42 tracked three major DNS tunneling campaigns (TrkCdn, SecShow, Savvy Seahorse) through 2024, showing the technique's continued prevalence.\n\n## When to Use\n\n- When investigating security incidents that require analyzing network covert channels in malware\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+ with `scapy`, `dpkt`, `dnslib`\n- Wireshark\u002Ftshark for PCAP analysis\n- Zeek (formerly Bro) for network monitoring\n- DNS query logging infrastructure\n- Understanding of DNS, ICMP, HTTP protocols at packet level\n\n## Workflow\n\n### Step 1: DNS Tunneling Detection\n\n```python\n#!\u002Fusr\u002Fbin\u002Fenv python3\n\"\"\"Detect DNS tunneling and covert channels in network traffic.\"\"\"\nimport sys\nimport json\nimport math\nfrom collections import Counter, defaultdict\n\ntry:\n    from scapy.all import rdpcap, DNS, DNSQR, DNSRR, IP, ICMP\nexcept ImportError:\n    print(\"pip install scapy\")\n    sys.exit(1)\n\ndef entropy(data):\n    if not data:\n        return 0\n    freq = Counter(data)\n    length = len(data)\n    return -sum((c\u002Flength) * math.log2(c\u002Flength) for c in freq.values())\n\ndef analyze_dns_tunneling(pcap_path):\n    \"\"\"Detect DNS tunneling indicators in PCAP.\"\"\"\n    packets = rdpcap(pcap_path)\n    domain_stats = defaultdict(lambda: {\n        \"queries\": 0, \"total_qname_len\": 0, \"subdomain_lengths\": [],\n        \"query_types\": Counter(), \"unique_subdomains\": set(),\n    })\n\n    for pkt in packets:\n        if pkt.haslayer(DNS) and pkt.haslayer(DNSQR):\n            qname = pkt[DNSQR].qname.decode('utf-8', errors='replace').rstrip('.')\n            qtype = pkt[DNSQR].qtype\n\n            parts = qname.split('.')\n            if len(parts) >= 3:\n                base_domain = '.'.join(parts[-2:])\n                subdomain = '.'.join(parts[:-2])\n\n                stats = domain_stats[base_domain]\n                stats[\"queries\"] += 1\n                stats[\"total_qname_len\"] += len(qname)\n                stats[\"subdomain_lengths\"].append(len(subdomain))\n                stats[\"query_types\"][qtype] += 1\n                stats[\"unique_subdomains\"].add(subdomain)\n\n    # Score domains for tunneling indicators\n    suspicious = []\n    for domain, stats in domain_stats.items():\n        if stats[\"queries\"] \u003C 5:\n            continue\n\n        avg_subdomain_len = (sum(stats[\"subdomain_lengths\"]) \u002F\n                             len(stats[\"subdomain_lengths\"]))\n        unique_ratio = len(stats[\"unique_subdomains\"]) \u002F stats[\"queries\"]\n\n        # Calculate subdomain entropy\n        all_subdomains = ''.join(stats[\"unique_subdomains\"])\n        sub_entropy = entropy(all_subdomains)\n\n        score = 0\n        reasons = []\n\n        if avg_subdomain_len > 30:\n            score += 30\n            reasons.append(f\"Long subdomains (avg {avg_subdomain_len:.0f} chars)\")\n        if unique_ratio > 0.9:\n            score += 25\n            reasons.append(f\"High uniqueness ({unique_ratio:.2%})\")\n        if sub_entropy > 4.0:\n            score += 25\n            reasons.append(f\"High entropy ({sub_entropy:.2f})\")\n        if stats[\"query_types\"].get(16, 0) > 10:  # TXT records\n            score += 20\n            reasons.append(f\"Many TXT queries ({stats['query_types'][16]})\")\n\n        if score >= 50:\n            suspicious.append({\n                \"domain\": domain,\n                \"score\": score,\n                \"queries\": stats[\"queries\"],\n                \"avg_subdomain_length\": round(avg_subdomain_len, 1),\n                \"unique_subdomains\": len(stats[\"unique_subdomains\"]),\n                \"subdomain_entropy\": round(sub_entropy, 2),\n                \"reasons\": reasons,\n            })\n\n    return sorted(suspicious, key=lambda x: -x[\"score\"])\n\ndef analyze_icmp_tunneling(pcap_path):\n    \"\"\"Detect ICMP tunneling in PCAP.\"\"\"\n    packets = rdpcap(pcap_path)\n    icmp_stats = defaultdict(lambda: {\"count\": 0, \"payload_sizes\": [], \"payloads\": []})\n\n    for pkt in packets:\n        if pkt.haslayer(ICMP) and pkt.haslayer(IP):\n            src = pkt[IP].src\n            dst = pkt[IP].dst\n            key = f\"{src}->{dst}\"\n\n            payload = bytes(pkt[ICMP].payload)\n            icmp_stats[key][\"count\"] += 1\n            icmp_stats[key][\"payload_sizes\"].append(len(payload))\n            if len(payload) > 64:\n                icmp_stats[key][\"payloads\"].append(payload[:100])\n\n    suspicious = []\n    for flow, stats in icmp_stats.items():\n        if stats[\"count\"] \u003C 5:\n            continue\n        avg_size = sum(stats[\"payload_sizes\"]) \u002F len(stats[\"payload_sizes\"])\n        if avg_size > 64 or stats[\"count\"] > 100:\n            suspicious.append({\n                \"flow\": flow,\n                \"packets\": stats[\"count\"],\n                \"avg_payload_size\": round(avg_size, 1),\n                \"reason\": \"Large\u002Ffrequent ICMP payloads suggest tunneling\",\n            })\n\n    return suspicious\n\nif __name__ == \"__main__\":\n    if len(sys.argv) \u003C 2:\n        print(f\"Usage: {sys.argv[0]} \u003Cpcap_file>\")\n        sys.exit(1)\n\n    print(\"[+] DNS Tunneling Analysis\")\n    dns_results = analyze_dns_tunneling(sys.argv[1])\n    for r in dns_results:\n        print(f\"  {r['domain']} (score: {r['score']})\")\n        for reason in r['reasons']:\n            print(f\"    - {reason}\")\n\n    print(\"\\n[+] ICMP Tunneling Analysis\")\n    icmp_results = analyze_icmp_tunneling(sys.argv[1])\n    for r in icmp_results:\n        print(f\"  {r['flow']}: {r['reason']}\")\n```\n\n## Validation Criteria\n\n- DNS tunneling detected via entropy, subdomain length, and query volume analysis\n- ICMP covert channels identified through payload size anomalies\n- Tunneling domains distinguished from legitimate CDN\u002Fcloud traffic\n- Data exfiltration volume estimated from captured traffic\n- C2 communication patterns and beaconing intervals extracted\n\n## References\n\n- [Palo Alto Unit42 - DNS Tunneling Campaigns](https:\u002F\u002Funit42.paloaltonetworks.com\u002Fthree-dns-tunneling-campaigns\u002F)\n- [Elastic - Detecting Covert Data Exfiltration](https:\u002F\u002Fwww.elastic.co\u002Fblog\u002Felastic-security-detecting-covert-data-exfiltration)\n- [Vectra AI - ICMP Tunnel Detection](https:\u002F\u002Fwww.vectra.ai\u002Fdetections\u002Ficmp-tunnel)\n- [MITRE ATT&CK T1071 - Application Layer Protocol](https:\u002F\u002Fattack.mitre.org\u002Ftechniques\u002FT1071\u002F)\n","# Analyzing Network Covert Channels in Malware\n\n## Overview\n\nMalware uses covert channels to disguise C2 communication and data exfiltration within legitimate-looking network traffic. DNS tunneling encodes data in DNS queries and responses (used by tools like iodine, dnscat2, and malware families like FrameworkPOS). ICMP tunneling hides data in echo request\u002Freply payloads (icmpsh, ptunnel). HTTP covert channels embed C2 data in headers, cookies, or steganographic images. Protocol abuse exploits allowed protocols to bypass firewalls. DNS tunneling detection achieves 99%+ recall with modern ML-based approaches, though low-throughput exfiltration remains challenging. Palo Alto Unit42 tracked three major DNS tunneling campaigns (TrkCdn, SecShow, Savvy Seahorse) through 2024, showing the technique's continued prevalence.\n\n## When to Use\n\n- When investigating security incidents that require analyzing network covert channels in malware\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+ with `scapy`, `dpkt`, `dnslib`\n- Wireshark\u002Ftshark for PCAP analysis\n- Zeek (formerly Bro) for network monitoring\n- DNS query logging infrastructure\n- Understanding of DNS, ICMP, HTTP protocols at packet level\n\n## Workflow\n\n### Step 1: DNS Tunneling Detection\n\n```python\n#!\u002Fusr\u002Fbin\u002Fenv python3\n\"\"\"Detect DNS tunneling and covert channels in network traffic.\"\"\"\nimport sys\nimport json\nimport math\nfrom collections import Counter, defaultdict\n\ntry:\n    from scapy.all import rdpcap, DNS, DNSQR, DNSRR, IP, ICMP\nexcept ImportError:\n    print(\"pip install scapy\")\n    sys.exit(1)\n\ndef entropy(data):\n    if not data:\n        return 0\n    freq = Counter(data)\n    length = len(data)\n    return -sum((c\u002Flength) * math.log2(c\u002Flength) for c in freq.values())\n\ndef analyze_dns_tunneling(pcap_path):\n    \"\"\"Detect DNS tunneling indicators in PCAP.\"\"\"\n    packets = rdpcap(pcap_path)\n    domain_stats = defaultdict(lambda: {\n        \"queries\": 0, \"total_qname_len\": 0, \"subdomain_lengths\": [],\n        \"query_types\": Counter(), \"unique_subdomains\": set(),\n    })\n\n    for pkt in packets:\n        if pkt.haslayer(DNS) and pkt.haslayer(DNSQR):\n            qname = pkt[DNSQR].qname.decode('utf-8', errors='replace').rstrip('.')\n            qtype = pkt[DNSQR].qtype\n\n            parts = qname.split('.')\n            if len(parts) >= 3:\n                base_domain = '.'.join(parts[-2:])\n                subdomain = '.'.join(parts[:-2])\n\n                stats = domain_stats[base_domain]\n                stats[\"queries\"] += 1\n                stats[\"total_qname_len\"] += len(qname)\n                stats[\"subdomain_lengths\"].append(len(subdomain))\n                stats[\"query_types\"][qtype] += 1\n                stats[\"unique_subdomains\"].add(subdomain)\n\n    # Score domains for tunneling indicators\n    suspicious = []\n    for domain, stats in domain_stats.items():\n        if stats[\"queries\"] \u003C 5:\n            continue\n\n        avg_subdomain_len = (sum(stats[\"subdomain_lengths\"]) \u002F\n                             len(stats[\"subdomain_lengths\"]))\n        unique_ratio = len(stats[\"unique_subdomains\"]) \u002F stats[\"queries\"]\n\n        # Calculate subdomain entropy\n        all_subdomains = ''.join(stats[\"unique_subdomains\"])\n        sub_entropy = entropy(all_subdomains)\n\n        score = 0\n        reasons = []\n\n        if avg_subdomain_len > 30:\n            score += 30\n            reasons.append(f\"Long subdomains (avg {avg_subdomain_len:.0f} chars)\")\n        if unique_ratio > 0.9:\n            score += 25\n            reasons.append(f\"High uniqueness ({unique_ratio:.2%})\")\n        if sub_entropy > 4.0:\n            score += 25\n            reasons.append(f\"High entropy ({sub_entropy:.2f})\")\n        if stats[\"query_types\"].get(16, 0) > 10:  # TXT records\n            score += 20\n            reasons.append(f\"Many TXT queries ({stats['query_types'][16]})\")\n\n        if score >= 50:\n            suspicious.append({\n                \"domain\": domain,\n                \"score\": score,\n                \"queries\": stats[\"queries\"],\n                \"avg_subdomain_length\": round(avg_subdomain_len, 1),\n                \"unique_subdomains\": len(stats[\"unique_subdomains\"]),\n                \"subdomain_entropy\": round(sub_entropy, 2),\n                \"reasons\": reasons,\n            })\n\n    return sorted(suspicious, key=lambda x: -x[\"score\"])\n\ndef analyze_icmp_tunneling(pcap_path):\n    \"\"\"Detect ICMP tunneling in PCAP.\"\"\"\n    packets = rdpcap(pcap_path)\n    icmp_stats = defaultdict(lambda: {\"count\": 0, \"payload_sizes\": [], \"payloads\": []})\n\n    for pkt in packets:\n        if pkt.haslayer(ICMP) and pkt.haslayer(IP):\n            src = pkt[IP].src\n            dst = pkt[IP].dst\n            key = f\"{src}->{dst}\"\n\n            payload = bytes(pkt[ICMP].payload)\n            icmp_stats[key][\"count\"] += 1\n            icmp_stats[key][\"payload_sizes\"].append(len(payload))\n            if len(payload) > 64:\n                icmp_stats[key][\"payloads\"].append(payload[:100])\n\n    suspicious = []\n    for flow, stats in icmp_stats.items():\n        if stats[\"count\"] \u003C 5:\n            continue\n        avg_size = sum(stats[\"payload_sizes\"]) \u002F len(stats[\"payload_sizes\"])\n        if avg_size > 64 or stats[\"count\"] > 100:\n            suspicious.append({\n                \"flow\": flow,\n                \"packets\": stats[\"count\"],\n                \"avg_payload_size\": round(avg_size, 1),\n                \"reason\": \"Large\u002Ffrequent ICMP payloads suggest tunneling\",\n            })\n\n    return suspicious\n\nif __name__ == \"__main__\":\n    if len(sys.argv) \u003C 2:\n        print(f\"Usage: {sys.argv[0]} \u003Cpcap_file>\")\n        sys.exit(1)\n\n    print(\"[+] DNS Tunneling Analysis\")\n    dns_results = analyze_dns_tunneling(sys.argv[1])\n    for r in dns_results:\n        print(f\"  {r['domain']} (score: {r['score']})\")\n        for reason in r['reasons']:\n            print(f\"    - {reason}\")\n\n    print(\"\\n[+] ICMP Tunneling Analysis\")\n    icmp_results = analyze_icmp_tunneling(sys.argv[1])\n    for r in icmp_results:\n        print(f\"  {r['flow']}: {r['reason']}\")\n```\n\n## Validation Criteria\n\n- DNS tunneling detected via entropy, subdomain length, and query volume analysis\n- ICMP covert channels identified through payload size anomalies\n- Tunneling domains distinguished from legitimate CDN\u002Fcloud traffic\n- Data exfiltration volume estimated from captured traffic\n- C2 communication patterns and beaconing intervals extracted\n\n## References\n\n- [Palo Alto Unit42 - DNS Tunneling Campaigns](https:\u002F\u002Funit42.paloaltonetworks.com\u002Fthree-dns-tunneling-campaigns\u002F)\n- [Elastic - Detecting Covert Data Exfiltration](https:\u002F\u002Fwww.elastic.co\u002Fblog\u002Felastic-security-detecting-covert-data-exfiltration)\n- [Vectra AI - ICMP Tunnel Detection](https:\u002F\u002Fwww.vectra.ai\u002Fdetections\u002Ficmp-tunnel)\n- [MITRE ATT&CK T1071 - Application Layer Protocol](https:\u002F\u002Fattack.mitre.org\u002Ftechniques\u002FT1071\u002F)\n",{"name":5,"domain":21,"description":7},"main","a91f1293287ffa9f4f1b8f486cd6e835a1568c84","skills\u002Fnet-traffic-net","https:\u002F\u002Fgithub.com\u002FDCx7C5\u002Fai-marketplace","https:\u002F\u002Fgithub.com\u002FDCx7C5","#Python","2026-09-01 11:21:00",{"source":8,"skillName":5,"commands":42,"prompt":52,"zipAvailable":53},[43,46,49],{"tool":44,"command":45},"npx","npx skills add DCx7C5\u002Fai-marketplace --skill net-traffic-net",{"tool":47,"command":48},"bunx","bunx skills add DCx7C5\u002Fai-marketplace --skill net-traffic-net",{"tool":50,"command":51},"pnpm","pnpm dlx skills add DCx7C5\u002Fai-marketplace --skill net-traffic-net","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FDCx7C5\u002Fai-marketplace\u002Fblob\u002Fmain\u002Fskills\u002Fnet-traffic-net\u002FSKILL.md",true,[55],{"path":56,"resourceType":57,"resourceTypeDesc":58,"sizeBytes":59,"sourceHtmlUrl":60},"index.json","REFERENCE","参考文档",511,"https:\u002F\u002Fgithub.com\u002FDCx7C5\u002Fai-marketplace\u002Fblob\u002Fmain\u002Fskills\u002Fnet-traffic-net\u002Findex.json",[62,87,109,131,153,169],{"skillNo":63,"name":64,"nameZh":6,"description":65,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":66,"categories":67,"keywords":68,"publishedAt":29},"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",[],[69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,21,86],"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":88,"name":89,"nameZh":6,"description":90,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":91,"categories":92,"keywords":93,"publishedAt":29},"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",[],[94,69,95,72,96,97,19,21,98,99,100,101,102,103,74,104,105,106,107,108],"exploit","services wmi exec exploit","exec","remote","Bash","analysis","team","across","crackmapexec","native","command","lateral movement","read","lateral-movement","Impacket",{"skillNo":110,"name":111,"nameZh":6,"description":112,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":113,"categories":114,"keywords":115,"publishedAt":29},"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",[],[116,117,118,119,120,121,21,98,122,99,123,124,74,106,125,126,127,128,129,130],"artifact","registry","registry artifact extract","extract","evidence-collection","artifact-analysis","collection","analyze","uncover","software","activity","digital-forensics","windows-registry","regripper","sonnet",{"skillNo":132,"name":133,"nameZh":6,"description":134,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":135,"categories":136,"keywords":137,"publishedAt":29},"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",[],[70,138,139,140,141,142,143,75,144,21,86,145,146,147,148,149,150,151,152,74],"processes token impersonate detect","token","processes","attacks","windows","escalation","techniques","privilege-escalation","seimpersonate","token-impersonation","printspoofer","exploitation","potato","impersonation","via",{"skillNo":154,"name":155,"nameZh":6,"description":156,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":157,"categories":158,"keywords":159,"publishedAt":29},"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",[],[160,161,70,162,140,142,163,75,164,144,21,86,99,165,166,167,168,152,74,76],"thread","processes thread injection detect","injection","memory","process-injection","api","apc-injection","thread-injection","nt-api",{"skillNo":170,"name":171,"nameZh":6,"description":172,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":173,"categories":174,"keywords":175,"publishedAt":29},"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",[],[176,70,140,177,163,75,178,179,21,86,180,181,182,183,184,185,74,76,186,187],"processes hollow technique detect","tree","memory-forensics","process-hollow","peb","runpe","image","pe-header","in memory","with","mismatched","headers"]