[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71DW2W3MS7N6ZSH0ZAF2EC7":3,"skill-same:01M71DW2W3MS7N6ZSH0ZAF2EC7":58},{"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":26,"linkOnly":27,"markdownContent":28,"markdownBody":29,"frontMatter":30,"refName":31,"commitSha":32,"skillDirectory":33,"forksCount":14,"repositoryHtmlUrl":34,"repositoryHomepageUrl":6,"ownerHtmlUrl":35,"ownerBlogUrl":6,"ownerDescription":36,"updatedAt":37,"install":38,"resources":51},"01M71DW2W3MS7N6ZSH0ZAF2EC7","intel-ioc-tilcm",null,"- [CISA Known Exploited Vulnerabilities](https:\u002F\u002Fwww.","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\u002Fintel-ioc-tilcm\u002FSKILL.md",[],[19,20,21,22,23,24,25],"ioc","intel","intel ioc tilcm","cybersecurity","vulnerabilities","https","CISA","2026-11-10 10:12:30",false,"---\nname: intel-ioc-tilcm\ndescription: \"- [CISA Known Exploited Vulnerabilities](https:\u002F\u002Fwww.\"\ndomain: cybersecurity\n---\n\n# Implementing Threat Intelligence Lifecycle Management\n\n## Overview\n\nThe threat intelligence lifecycle is a structured, iterative process for transforming raw data into actionable intelligence. Based on the intelligence cycle used by military and government agencies, it comprises six phases: Direction (requirements gathering), Collection (data acquisition), Processing (normalization and deduplication), Analysis (contextualization and assessment), Dissemination (distribution to stakeholders), and Feedback (evaluation and refinement). This skill covers building each phase with tooling, metrics, and integration points for a mature CTI program.\n\n## When to Use\n\n- When deploying or configuring implementing threat intelligence lifecycle management capabilities in your environment\n- When establishing security controls aligned to compliance requirements\n- When building or improving security architecture for this domain\n- When conducting security assessments that require this implementation\n\n## Prerequisites\n\n- Python 3.9+ with `pymisp`, `stix2`, `requests`, `pandas` libraries\n- MISP or OpenCTI as threat intelligence platform\n- Ticketing system (Jira, ServiceNow) for requirements management\n- SIEM integration (Splunk, Elastic) for indicator operationalization\n- Understanding of intelligence analysis techniques (ACH, Diamond Model)\n\n## Key Concepts\n\n### Intelligence Requirements (IR)\n\nPriority Intelligence Requirements (PIRs) define what the organization needs to know. Examples: Which threat actors target our sector? What vulnerabilities are being actively exploited? Are our brand or credentials being traded on dark web? PIRs drive collection planning and ensure intelligence production is relevant.\n\n### Collection Management Framework\n\nA collection management framework maps intelligence requirements to collection sources, tracks collection gaps, and ensures coverage across the threat landscape. Sources include OSINT, commercial feeds, ISAC sharing, internal telemetry, and human intelligence from industry contacts.\n\n### Intelligence Levels\n\nStrategic intelligence informs executive decision-making (threat landscape, risk trends, geopolitical context). Operational intelligence supports security operations (campaign tracking, actor TTPs, attack timing). Tactical intelligence enables immediate defense (IOCs, detection rules, blocklists).\n\n## Workflow\n\n### Step 1: Define Intelligence Requirements\n\n```python\nimport json\nfrom datetime import datetime\nfrom enum import Enum\n\nclass Priority(Enum):\n    CRITICAL = 1\n    HIGH = 2\n    MEDIUM = 3\n    LOW = 4\n\nclass IntelligenceRequirement:\n    def __init__(self, requirement_id, question, priority, stakeholder,\n                 intelligence_level, collection_sources=None):\n        self.id = requirement_id\n        self.question = question\n        self.priority = priority\n        self.stakeholder = stakeholder\n        self.level = intelligence_level\n        self.sources = collection_sources or []\n        self.created = datetime.now().isoformat()\n        self.status = \"active\"\n        self.last_answered = None\n\n    def to_dict(self):\n        return {\n            \"id\": self.id,\n            \"question\": self.question,\n            \"priority\": self.priority.name,\n            \"stakeholder\": self.stakeholder,\n            \"intelligence_level\": self.level,\n            \"collection_sources\": self.sources,\n            \"created\": self.created,\n            \"status\": self.status,\n            \"last_answered\": self.last_answered,\n        }\n\nclass RequirementsManager:\n    def __init__(self):\n        self.requirements = []\n\n    def add_requirement(self, requirement):\n        self.requirements.append(requirement)\n        print(f\"[+] Added IR-{requirement.id}: {requirement.question[:60]}...\")\n\n    def get_active_requirements(self, priority=None, level=None):\n        filtered = [r for r in self.requirements if r.status == \"active\"]\n        if priority:\n            filtered = [r for r in filtered if r.priority == priority]\n        if level:\n            filtered = [r for r in filtered if r.level == level]\n        return filtered\n\n    def export_requirements(self, output_file=\"intelligence_requirements.json\"):\n        data = [r.to_dict() for r in self.requirements]\n        with open(output_file, \"w\") as f:\n            json.dump(data, f, indent=2)\n        print(f\"[+] Exported {len(data)} requirements to {output_file}\")\n\n# Define organizational PIRs\nmgr = RequirementsManager()\nmgr.add_requirement(IntelligenceRequirement(\n    \"PIR-001\", \"Which threat actors are actively targeting our sector?\",\n    Priority.CRITICAL, \"CISO\", \"strategic\",\n    [\"MITRE ATT&CK\", \"ISAC feeds\", \"Vendor reports\"],\n))\nmgr.add_requirement(IntelligenceRequirement(\n    \"PIR-002\", \"What vulnerabilities are being actively exploited in the wild?\",\n    Priority.CRITICAL, \"Vulnerability Management\", \"operational\",\n    [\"CISA KEV\", \"Exploit-DB\", \"VulnCheck\", \"Shodan\"],\n))\nmgr.add_requirement(IntelligenceRequirement(\n    \"PIR-003\", \"Are any organization credentials or data exposed on dark web?\",\n    Priority.HIGH, \"SOC Manager\", \"tactical\",\n    [\"Dark web monitoring\", \"Paste site monitoring\", \"Breach databases\"],\n))\nmgr.add_requirement(IntelligenceRequirement(\n    \"PIR-004\", \"What are the emerging attack techniques against cloud infrastructure?\",\n    Priority.HIGH, \"Cloud Security\", \"operational\",\n    [\"ATT&CK Cloud matrix\", \"Vendor advisories\", \"ISAC bulletins\"],\n))\nmgr.export_requirements()\n```\n\n### Step 2: Build Collection Pipeline\n\n```python\nimport requests\nfrom datetime import datetime, timedelta\n\nclass CollectionPipeline:\n    def __init__(self, config):\n        self.config = config\n        self.collected_data = []\n\n    def collect_cisa_kev(self):\n        \"\"\"Collect CISA Known Exploited Vulnerabilities catalog.\"\"\"\n        url = \"https:\u002F\u002Fwww.cisa.gov\u002Fsites\u002Fdefault\u002Ffiles\u002Ffeeds\u002Fknown_exploited_vulnerabilities.json\"\n        resp = requests.get(url, timeout=30)\n        if resp.status_code == 200:\n            data = resp.json()\n            vulns = data.get(\"vulnerabilities\", [])\n            self.collected_data.append({\n                \"source\": \"CISA KEV\",\n                \"type\": \"vulnerability\",\n                \"count\": len(vulns),\n                \"collected_at\": datetime.now().isoformat(),\n                \"data\": vulns,\n            })\n            print(f\"[+] CISA KEV: {len(vulns)} known exploited vulnerabilities\")\n            return vulns\n        return []\n\n    def collect_otx_pulses(self, api_key, days=7):\n        \"\"\"Collect recent OTX pulses.\"\"\"\n        headers = {\"X-OTX-API-KEY\": api_key}\n        since = (datetime.now() - timedelta(days=days)).isoformat()\n        url = f\"https:\u002F\u002Fotx.alienvault.com\u002Fapi\u002Fv1\u002Fpulses\u002Fsubscribed?modified_since={since}\"\n        resp = requests.get(url, headers=headers, timeout=30)\n        if resp.status_code == 200:\n            pulses = resp.json().get(\"results\", [])\n            self.collected_data.append({\n                \"source\": \"AlienVault OTX\",\n                \"type\": \"threat_intelligence\",\n                \"count\": len(pulses),\n                \"collected_at\": datetime.now().isoformat(),\n            })\n            print(f\"[+] OTX: {len(pulses)} pulses in last {days} days\")\n            return pulses\n        return []\n\n    def collect_abuse_ch(self):\n        \"\"\"Collect recent malware samples from MalwareBazaar.\"\"\"\n        url = \"https:\u002F\u002Fmb-api.abuse.ch\u002Fapi\u002Fv1\u002F\"\n        resp = requests.post(url, data={\"query\": \"get_recent\", \"selector\": \"time\"}, timeout=30)\n        if resp.status_code == 200:\n            data = resp.json().get(\"data\", [])\n            self.collected_data.append({\n                \"source\": \"MalwareBazaar\",\n                \"type\": \"malware_samples\",\n                \"count\": len(data),\n                \"collected_at\": datetime.now().isoformat(),\n            })\n            print(f\"[+] MalwareBazaar: {len(data)} recent samples\")\n            return data\n        return []\n\n    def get_collection_summary(self):\n        summary = {\n            \"total_sources\": len(self.collected_data),\n            \"total_items\": sum(d.get(\"count\", 0) for d in self.collected_data),\n            \"sources\": [\n                {\"name\": d[\"source\"], \"type\": d[\"type\"], \"count\": d[\"count\"]}\n                for d in self.collected_data\n            ],\n        }\n        return summary\n\npipeline = CollectionPipeline({})\npipeline.collect_cisa_kev()\npipeline.collect_abuse_ch()\nprint(json.dumps(pipeline.get_collection_summary(), indent=2))\n```\n\n### Step 3: Process and Normalize Data\n\n```python\nclass IntelligenceProcessor:\n    def __init__(self):\n        self.processed_items = []\n        self.dedup_hashes = set()\n\n    def process_collection(self, raw_data, source_name):\n        \"\"\"Normalize and deduplicate collected intelligence.\"\"\"\n        processed = []\n        duplicates = 0\n\n        for item in raw_data:\n            normalized = self._normalize(item, source_name)\n            if normalized:\n                item_hash = self._compute_hash(normalized)\n                if item_hash not in self.dedup_hashes:\n                    self.dedup_hashes.add(item_hash)\n                    normalized[\"processed_at\"] = datetime.now().isoformat()\n                    processed.append(normalized)\n                else:\n                    duplicates += 1\n\n        self.processed_items.extend(processed)\n        print(f\"[+] Processed {len(processed)} items from {source_name} \"\n              f\"({duplicates} duplicates removed)\")\n        return processed\n\n    def _normalize(self, item, source):\n        \"\"\"Normalize item to standard format.\"\"\"\n        return {\n            \"source\": source,\n            \"type\": item.get(\"type\", \"unknown\"),\n            \"value\": item.get(\"value\", item.get(\"indicator\", \"\")),\n            \"confidence\": item.get(\"confidence\", 50),\n            \"tlp\": item.get(\"tlp\", \"green\"),\n            \"tags\": item.get(\"tags\", []),\n            \"first_seen\": item.get(\"first_seen\", item.get(\"date_added\", \"\")),\n            \"raw\": item,\n        }\n\n    def _compute_hash(self, item):\n        import hashlib\n        key = f\"{item['type']}:{item['value']}:{item['source']}\"\n        return hashlib.sha256(key.encode()).hexdigest()\n\nprocessor = IntelligenceProcessor()\n```\n\n### Step 4: Analyze and Produce Intelligence\n\n```python\nclass IntelligenceAnalyzer:\n    def __init__(self, requirements, processed_data):\n        self.requirements = requirements\n        self.data = processed_data\n\n    def answer_requirement(self, requirement_id):\n        \"\"\"Produce intelligence answering a specific requirement.\"\"\"\n        req = next((r for r in self.requirements if r.id == requirement_id), None)\n        if not req:\n            return None\n\n        # Filter relevant data based on requirement type\n        relevant = self.data  # In practice, filter by requirement topic\n        analysis = {\n            \"requirement_id\": requirement_id,\n            \"question\": req.question,\n            \"intelligence_level\": req.level,\n            \"data_points_analyzed\": len(relevant),\n            \"produced_at\": datetime.now().isoformat(),\n            \"key_findings\": [],\n            \"confidence\": \"medium\",\n            \"recommendations\": [],\n        }\n        return analysis\n\n    def produce_daily_brief(self):\n        \"\"\"Produce daily threat intelligence brief.\"\"\"\n        brief = {\n            \"date\": datetime.now().strftime(\"%Y-%m-%d\"),\n            \"total_items_processed\": len(self.data),\n            \"highlights\": [],\n            \"active_requirements_status\": [\n                {\"id\": r.id, \"question\": r.question[:80], \"status\": r.status}\n                for r in self.requirements if r.status == \"active\"\n            ],\n        }\n        return brief\n```\n\n### Step 5: Disseminate and Track Feedback\n\n```python\nclass IntelligenceDisseminator:\n    def __init__(self):\n        self.distribution_log = []\n\n    def distribute_report(self, report, channels, classification=\"TLP:GREEN\"):\n        \"\"\"Distribute intelligence report to appropriate channels.\"\"\"\n        for channel in channels:\n            entry = {\n                \"report_id\": report.get(\"requirement_id\", \"daily\"),\n                \"channel\": channel,\n                \"classification\": classification,\n                \"distributed_at\": datetime.now().isoformat(),\n                \"status\": \"sent\",\n            }\n            self.distribution_log.append(entry)\n            print(f\"  [+] Distributed to {channel}\")\n\n    def collect_feedback(self, report_id, stakeholder, rating, comments=\"\"):\n        \"\"\"Collect stakeholder feedback on intelligence product.\"\"\"\n        feedback = {\n            \"report_id\": report_id,\n            \"stakeholder\": stakeholder,\n            \"rating\": rating,  # 1-5\n            \"comments\": comments,\n            \"received_at\": datetime.now().isoformat(),\n        }\n        print(f\"[+] Feedback received from {stakeholder}: {rating}\u002F5\")\n        return feedback\n\n    def calculate_metrics(self):\n        \"\"\"Calculate CTI program performance metrics.\"\"\"\n        metrics = {\n            \"total_products_distributed\": len(self.distribution_log),\n            \"distribution_by_channel\": {},\n        }\n        for entry in self.distribution_log:\n            channel = entry[\"channel\"]\n            if channel not in metrics[\"distribution_by_channel\"]:\n                metrics[\"distribution_by_channel\"][channel] = 0\n            metrics[\"distribution_by_channel\"][channel] += 1\n        return metrics\n\ndisseminator = IntelligenceDisseminator()\n```\n\n## Validation Criteria\n\n- Intelligence requirements defined with priorities and stakeholders\n- Collection pipeline gathering from multiple sources\n- Processing deduplicates and normalizes data correctly\n- Analysis produces intelligence answering specific requirements\n- Dissemination reaches appropriate stakeholders through right channels\n- Feedback mechanism captures and incorporates stakeholder input\n\n## References\n\n- [SANS: Cyber Threat Intelligence Lifecycle](https:\u002F\u002Fwww.sans.org\u002Fwhite-papers\u002F36297\u002F)\n- [CISA: Cybersecurity Automation Best Practices](https:\u002F\u002Fwww.cisa.gov\u002Fsites\u002Fdefault\u002Ffiles\u002Fpublications\u002FOperational%20Value%20of%20IOCs_508c.pdf)\n- [CyCognito: Threat Intelligence Lifecycle](https:\u002F\u002Fwww.cycognito.com\u002Flearn\u002Fthreat-intelligence\u002F)\n- [MISP Project](https:\u002F\u002Fwww.misp-project.org\u002F)\n- [STIX\u002FTAXII Documentation](https:\u002F\u002Foasis-open.github.io\u002Fcti-documentation\u002F)\n- [CISA Known Exploited Vulnerabilities](https:\u002F\u002Fwww.cisa.gov\u002Fknown-exploited-vulnerabilities-catalog)\n","# Implementing Threat Intelligence Lifecycle Management\n\n## Overview\n\nThe threat intelligence lifecycle is a structured, iterative process for transforming raw data into actionable intelligence. Based on the intelligence cycle used by military and government agencies, it comprises six phases: Direction (requirements gathering), Collection (data acquisition), Processing (normalization and deduplication), Analysis (contextualization and assessment), Dissemination (distribution to stakeholders), and Feedback (evaluation and refinement). This skill covers building each phase with tooling, metrics, and integration points for a mature CTI program.\n\n## When to Use\n\n- When deploying or configuring implementing threat intelligence lifecycle management capabilities in your environment\n- When establishing security controls aligned to compliance requirements\n- When building or improving security architecture for this domain\n- When conducting security assessments that require this implementation\n\n## Prerequisites\n\n- Python 3.9+ with `pymisp`, `stix2`, `requests`, `pandas` libraries\n- MISP or OpenCTI as threat intelligence platform\n- Ticketing system (Jira, ServiceNow) for requirements management\n- SIEM integration (Splunk, Elastic) for indicator operationalization\n- Understanding of intelligence analysis techniques (ACH, Diamond Model)\n\n## Key Concepts\n\n### Intelligence Requirements (IR)\n\nPriority Intelligence Requirements (PIRs) define what the organization needs to know. Examples: Which threat actors target our sector? What vulnerabilities are being actively exploited? Are our brand or credentials being traded on dark web? PIRs drive collection planning and ensure intelligence production is relevant.\n\n### Collection Management Framework\n\nA collection management framework maps intelligence requirements to collection sources, tracks collection gaps, and ensures coverage across the threat landscape. Sources include OSINT, commercial feeds, ISAC sharing, internal telemetry, and human intelligence from industry contacts.\n\n### Intelligence Levels\n\nStrategic intelligence informs executive decision-making (threat landscape, risk trends, geopolitical context). Operational intelligence supports security operations (campaign tracking, actor TTPs, attack timing). Tactical intelligence enables immediate defense (IOCs, detection rules, blocklists).\n\n## Workflow\n\n### Step 1: Define Intelligence Requirements\n\n```python\nimport json\nfrom datetime import datetime\nfrom enum import Enum\n\nclass Priority(Enum):\n    CRITICAL = 1\n    HIGH = 2\n    MEDIUM = 3\n    LOW = 4\n\nclass IntelligenceRequirement:\n    def __init__(self, requirement_id, question, priority, stakeholder,\n                 intelligence_level, collection_sources=None):\n        self.id = requirement_id\n        self.question = question\n        self.priority = priority\n        self.stakeholder = stakeholder\n        self.level = intelligence_level\n        self.sources = collection_sources or []\n        self.created = datetime.now().isoformat()\n        self.status = \"active\"\n        self.last_answered = None\n\n    def to_dict(self):\n        return {\n            \"id\": self.id,\n            \"question\": self.question,\n            \"priority\": self.priority.name,\n            \"stakeholder\": self.stakeholder,\n            \"intelligence_level\": self.level,\n            \"collection_sources\": self.sources,\n            \"created\": self.created,\n            \"status\": self.status,\n            \"last_answered\": self.last_answered,\n        }\n\nclass RequirementsManager:\n    def __init__(self):\n        self.requirements = []\n\n    def add_requirement(self, requirement):\n        self.requirements.append(requirement)\n        print(f\"[+] Added IR-{requirement.id}: {requirement.question[:60]}...\")\n\n    def get_active_requirements(self, priority=None, level=None):\n        filtered = [r for r in self.requirements if r.status == \"active\"]\n        if priority:\n            filtered = [r for r in filtered if r.priority == priority]\n        if level:\n            filtered = [r for r in filtered if r.level == level]\n        return filtered\n\n    def export_requirements(self, output_file=\"intelligence_requirements.json\"):\n        data = [r.to_dict() for r in self.requirements]\n        with open(output_file, \"w\") as f:\n            json.dump(data, f, indent=2)\n        print(f\"[+] Exported {len(data)} requirements to {output_file}\")\n\n# Define organizational PIRs\nmgr = RequirementsManager()\nmgr.add_requirement(IntelligenceRequirement(\n    \"PIR-001\", \"Which threat actors are actively targeting our sector?\",\n    Priority.CRITICAL, \"CISO\", \"strategic\",\n    [\"MITRE ATT&CK\", \"ISAC feeds\", \"Vendor reports\"],\n))\nmgr.add_requirement(IntelligenceRequirement(\n    \"PIR-002\", \"What vulnerabilities are being actively exploited in the wild?\",\n    Priority.CRITICAL, \"Vulnerability Management\", \"operational\",\n    [\"CISA KEV\", \"Exploit-DB\", \"VulnCheck\", \"Shodan\"],\n))\nmgr.add_requirement(IntelligenceRequirement(\n    \"PIR-003\", \"Are any organization credentials or data exposed on dark web?\",\n    Priority.HIGH, \"SOC Manager\", \"tactical\",\n    [\"Dark web monitoring\", \"Paste site monitoring\", \"Breach databases\"],\n))\nmgr.add_requirement(IntelligenceRequirement(\n    \"PIR-004\", \"What are the emerging attack techniques against cloud infrastructure?\",\n    Priority.HIGH, \"Cloud Security\", \"operational\",\n    [\"ATT&CK Cloud matrix\", \"Vendor advisories\", \"ISAC bulletins\"],\n))\nmgr.export_requirements()\n```\n\n### Step 2: Build Collection Pipeline\n\n```python\nimport requests\nfrom datetime import datetime, timedelta\n\nclass CollectionPipeline:\n    def __init__(self, config):\n        self.config = config\n        self.collected_data = []\n\n    def collect_cisa_kev(self):\n        \"\"\"Collect CISA Known Exploited Vulnerabilities catalog.\"\"\"\n        url = \"https:\u002F\u002Fwww.cisa.gov\u002Fsites\u002Fdefault\u002Ffiles\u002Ffeeds\u002Fknown_exploited_vulnerabilities.json\"\n        resp = requests.get(url, timeout=30)\n        if resp.status_code == 200:\n            data = resp.json()\n            vulns = data.get(\"vulnerabilities\", [])\n            self.collected_data.append({\n                \"source\": \"CISA KEV\",\n                \"type\": \"vulnerability\",\n                \"count\": len(vulns),\n                \"collected_at\": datetime.now().isoformat(),\n                \"data\": vulns,\n            })\n            print(f\"[+] CISA KEV: {len(vulns)} known exploited vulnerabilities\")\n            return vulns\n        return []\n\n    def collect_otx_pulses(self, api_key, days=7):\n        \"\"\"Collect recent OTX pulses.\"\"\"\n        headers = {\"X-OTX-API-KEY\": api_key}\n        since = (datetime.now() - timedelta(days=days)).isoformat()\n        url = f\"https:\u002F\u002Fotx.alienvault.com\u002Fapi\u002Fv1\u002Fpulses\u002Fsubscribed?modified_since={since}\"\n        resp = requests.get(url, headers=headers, timeout=30)\n        if resp.status_code == 200:\n            pulses = resp.json().get(\"results\", [])\n            self.collected_data.append({\n                \"source\": \"AlienVault OTX\",\n                \"type\": \"threat_intelligence\",\n                \"count\": len(pulses),\n                \"collected_at\": datetime.now().isoformat(),\n            })\n            print(f\"[+] OTX: {len(pulses)} pulses in last {days} days\")\n            return pulses\n        return []\n\n    def collect_abuse_ch(self):\n        \"\"\"Collect recent malware samples from MalwareBazaar.\"\"\"\n        url = \"https:\u002F\u002Fmb-api.abuse.ch\u002Fapi\u002Fv1\u002F\"\n        resp = requests.post(url, data={\"query\": \"get_recent\", \"selector\": \"time\"}, timeout=30)\n        if resp.status_code == 200:\n            data = resp.json().get(\"data\", [])\n            self.collected_data.append({\n                \"source\": \"MalwareBazaar\",\n                \"type\": \"malware_samples\",\n                \"count\": len(data),\n                \"collected_at\": datetime.now().isoformat(),\n            })\n            print(f\"[+] MalwareBazaar: {len(data)} recent samples\")\n            return data\n        return []\n\n    def get_collection_summary(self):\n        summary = {\n            \"total_sources\": len(self.collected_data),\n            \"total_items\": sum(d.get(\"count\", 0) for d in self.collected_data),\n            \"sources\": [\n                {\"name\": d[\"source\"], \"type\": d[\"type\"], \"count\": d[\"count\"]}\n                for d in self.collected_data\n            ],\n        }\n        return summary\n\npipeline = CollectionPipeline({})\npipeline.collect_cisa_kev()\npipeline.collect_abuse_ch()\nprint(json.dumps(pipeline.get_collection_summary(), indent=2))\n```\n\n### Step 3: Process and Normalize Data\n\n```python\nclass IntelligenceProcessor:\n    def __init__(self):\n        self.processed_items = []\n        self.dedup_hashes = set()\n\n    def process_collection(self, raw_data, source_name):\n        \"\"\"Normalize and deduplicate collected intelligence.\"\"\"\n        processed = []\n        duplicates = 0\n\n        for item in raw_data:\n            normalized = self._normalize(item, source_name)\n            if normalized:\n                item_hash = self._compute_hash(normalized)\n                if item_hash not in self.dedup_hashes:\n                    self.dedup_hashes.add(item_hash)\n                    normalized[\"processed_at\"] = datetime.now().isoformat()\n                    processed.append(normalized)\n                else:\n                    duplicates += 1\n\n        self.processed_items.extend(processed)\n        print(f\"[+] Processed {len(processed)} items from {source_name} \"\n              f\"({duplicates} duplicates removed)\")\n        return processed\n\n    def _normalize(self, item, source):\n        \"\"\"Normalize item to standard format.\"\"\"\n        return {\n            \"source\": source,\n            \"type\": item.get(\"type\", \"unknown\"),\n            \"value\": item.get(\"value\", item.get(\"indicator\", \"\")),\n            \"confidence\": item.get(\"confidence\", 50),\n            \"tlp\": item.get(\"tlp\", \"green\"),\n            \"tags\": item.get(\"tags\", []),\n            \"first_seen\": item.get(\"first_seen\", item.get(\"date_added\", \"\")),\n            \"raw\": item,\n        }\n\n    def _compute_hash(self, item):\n        import hashlib\n        key = f\"{item['type']}:{item['value']}:{item['source']}\"\n        return hashlib.sha256(key.encode()).hexdigest()\n\nprocessor = IntelligenceProcessor()\n```\n\n### Step 4: Analyze and Produce Intelligence\n\n```python\nclass IntelligenceAnalyzer:\n    def __init__(self, requirements, processed_data):\n        self.requirements = requirements\n        self.data = processed_data\n\n    def answer_requirement(self, requirement_id):\n        \"\"\"Produce intelligence answering a specific requirement.\"\"\"\n        req = next((r for r in self.requirements if r.id == requirement_id), None)\n        if not req:\n            return None\n\n        # Filter relevant data based on requirement type\n        relevant = self.data  # In practice, filter by requirement topic\n        analysis = {\n            \"requirement_id\": requirement_id,\n            \"question\": req.question,\n            \"intelligence_level\": req.level,\n            \"data_points_analyzed\": len(relevant),\n            \"produced_at\": datetime.now().isoformat(),\n            \"key_findings\": [],\n            \"confidence\": \"medium\",\n            \"recommendations\": [],\n        }\n        return analysis\n\n    def produce_daily_brief(self):\n        \"\"\"Produce daily threat intelligence brief.\"\"\"\n        brief = {\n            \"date\": datetime.now().strftime(\"%Y-%m-%d\"),\n            \"total_items_processed\": len(self.data),\n            \"highlights\": [],\n            \"active_requirements_status\": [\n                {\"id\": r.id, \"question\": r.question[:80], \"status\": r.status}\n                for r in self.requirements if r.status == \"active\"\n            ],\n        }\n        return brief\n```\n\n### Step 5: Disseminate and Track Feedback\n\n```python\nclass IntelligenceDisseminator:\n    def __init__(self):\n        self.distribution_log = []\n\n    def distribute_report(self, report, channels, classification=\"TLP:GREEN\"):\n        \"\"\"Distribute intelligence report to appropriate channels.\"\"\"\n        for channel in channels:\n            entry = {\n                \"report_id\": report.get(\"requirement_id\", \"daily\"),\n                \"channel\": channel,\n                \"classification\": classification,\n                \"distributed_at\": datetime.now().isoformat(),\n                \"status\": \"sent\",\n            }\n            self.distribution_log.append(entry)\n            print(f\"  [+] Distributed to {channel}\")\n\n    def collect_feedback(self, report_id, stakeholder, rating, comments=\"\"):\n        \"\"\"Collect stakeholder feedback on intelligence product.\"\"\"\n        feedback = {\n            \"report_id\": report_id,\n            \"stakeholder\": stakeholder,\n            \"rating\": rating,  # 1-5\n            \"comments\": comments,\n            \"received_at\": datetime.now().isoformat(),\n        }\n        print(f\"[+] Feedback received from {stakeholder}: {rating}\u002F5\")\n        return feedback\n\n    def calculate_metrics(self):\n        \"\"\"Calculate CTI program performance metrics.\"\"\"\n        metrics = {\n            \"total_products_distributed\": len(self.distribution_log),\n            \"distribution_by_channel\": {},\n        }\n        for entry in self.distribution_log:\n            channel = entry[\"channel\"]\n            if channel not in metrics[\"distribution_by_channel\"]:\n                metrics[\"distribution_by_channel\"][channel] = 0\n            metrics[\"distribution_by_channel\"][channel] += 1\n        return metrics\n\ndisseminator = IntelligenceDisseminator()\n```\n\n## Validation Criteria\n\n- Intelligence requirements defined with priorities and stakeholders\n- Collection pipeline gathering from multiple sources\n- Processing deduplicates and normalizes data correctly\n- Analysis produces intelligence answering specific requirements\n- Dissemination reaches appropriate stakeholders through right channels\n- Feedback mechanism captures and incorporates stakeholder input\n\n## References\n\n- [SANS: Cyber Threat Intelligence Lifecycle](https:\u002F\u002Fwww.sans.org\u002Fwhite-papers\u002F36297\u002F)\n- [CISA: Cybersecurity Automation Best Practices](https:\u002F\u002Fwww.cisa.gov\u002Fsites\u002Fdefault\u002Ffiles\u002Fpublications\u002FOperational%20Value%20of%20IOCs_508c.pdf)\n- [CyCognito: Threat Intelligence Lifecycle](https:\u002F\u002Fwww.cycognito.com\u002Flearn\u002Fthreat-intelligence\u002F)\n- [MISP Project](https:\u002F\u002Fwww.misp-project.org\u002F)\n- [STIX\u002FTAXII Documentation](https:\u002F\u002Foasis-open.github.io\u002Fcti-documentation\u002F)\n- [CISA Known Exploited Vulnerabilities](https:\u002F\u002Fwww.cisa.gov\u002Fknown-exploited-vulnerabilities-catalog)\n",{"name":5,"domain":22,"description":7},"main","a91f1293287ffa9f4f1b8f486cd6e835a1568c84","skills\u002Fintel-ioc-tilcm","https:\u002F\u002Fgithub.com\u002FDCx7C5\u002Fai-marketplace","https:\u002F\u002Fgithub.com\u002FDCx7C5","#Python","2026-09-01 11:21:00",{"source":8,"skillName":5,"commands":39,"prompt":49,"zipAvailable":50},[40,43,46],{"tool":41,"command":42},"npx","npx skills add DCx7C5\u002Fai-marketplace --skill intel-ioc-tilcm",{"tool":44,"command":45},"bunx","bunx skills add DCx7C5\u002Fai-marketplace --skill intel-ioc-tilcm",{"tool":47,"command":48},"pnpm","pnpm dlx skills add DCx7C5\u002Fai-marketplace --skill intel-ioc-tilcm","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FDCx7C5\u002Fai-marketplace\u002Fblob\u002Fmain\u002Fskills\u002Fintel-ioc-tilcm\u002FSKILL.md",true,[52],{"path":53,"resourceType":54,"resourceTypeDesc":55,"sizeBytes":56,"sourceHtmlUrl":57},"index.json","REFERENCE","参考文档",497,"https:\u002F\u002Fgithub.com\u002FDCx7C5\u002Fai-marketplace\u002Fblob\u002Fmain\u002Fskills\u002Fintel-ioc-tilcm\u002Findex.json",[59,84,107,129,151,167],{"skillNo":60,"name":61,"nameZh":6,"description":62,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":63,"categories":64,"keywords":65,"publishedAt":26},"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",[],[66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,22,83],"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":85,"name":86,"nameZh":6,"description":87,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":88,"categories":89,"keywords":90,"publishedAt":26},"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",[],[91,66,92,69,93,94,95,22,96,97,98,99,100,101,71,102,103,104,105,106],"exploit","services wmi exec exploit","exec","remote","traffic","Bash","analysis","team","across","crackmapexec","native","command","lateral movement","read","lateral-movement","Impacket",{"skillNo":108,"name":109,"nameZh":6,"description":110,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":111,"categories":112,"keywords":113,"publishedAt":26},"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",[],[114,115,116,117,118,119,22,96,120,97,121,122,71,104,123,124,125,126,127,128],"artifact","registry","registry artifact extract","extract","evidence-collection","artifact-analysis","collection","analyze","uncover","software","activity","digital-forensics","windows-registry","regripper","sonnet",{"skillNo":130,"name":131,"nameZh":6,"description":132,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":133,"categories":134,"keywords":135,"publishedAt":26},"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",[],[67,136,137,138,139,140,141,72,142,22,83,143,144,145,146,147,148,149,150,71],"processes token impersonate detect","token","processes","attacks","windows","escalation","techniques","privilege-escalation","seimpersonate","token-impersonation","printspoofer","exploitation","potato","impersonation","via",{"skillNo":152,"name":153,"nameZh":6,"description":154,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":155,"categories":156,"keywords":157,"publishedAt":26},"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",[],[158,159,67,160,138,140,161,72,162,142,22,83,97,163,164,165,166,150,71,73],"thread","processes thread injection detect","injection","memory","process-injection","api","apc-injection","thread-injection","nt-api",{"skillNo":168,"name":169,"nameZh":6,"description":170,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":171,"categories":172,"keywords":173,"publishedAt":26},"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",[],[174,67,138,175,161,72,176,177,22,83,178,179,180,181,182,183,71,73,184,185],"processes hollow technique detect","tree","memory-forensics","process-hollow","peb","runpe","image","pe-header","in memory","with","mismatched","headers"]