| name | Threat Hunting & IOC Analysis |
| description | IOC extraction, threat intelligence correlation, MITRE ATT&CK mapping, hunt hypothesis generation, and detection rule creation |
| version | 3.0.0 |
| author | Masriyan |
| tags | ["cybersecurity","threat-hunting","ioc","mitre-attack","threat-intelligence","sigma","detection","siem"] |
Threat Hunting & IOC Analysis
Purpose
Enable Claude to assist threat hunters with proactive threat detection, IOC extraction and normalization, MITRE ATT&CK mapping, hunt hypothesis generation, and converting threat intelligence into actionable detection rules across all major SIEM platforms.
Activation Triggers
This skill activates when the user asks about:
- Extracting IOCs from threat reports, emails, or security advisories
- Mapping behaviors or TTPs to MITRE ATT&CK framework
- Generating hunt hypotheses for a specific threat actor or technique
- Creating Sigma rules, Splunk SPL queries, KQL, or EQL
- Converting threat intelligence into SIEM detection queries
- STIX/TAXII or MISP-compatible indicator formatting
- ATT&CK Navigator layer creation
- Threat intelligence correlation across multiple sources
- Proactive threat hunting in a SIEM or EDR
Prerequisites
pip install requests pyyaml stix2 taxii2-client
Optional platforms:
- MISP — Threat intelligence sharing platform
- OpenCTI — Threat intelligence platform
- YARA — Pattern matching (→ Skill 05)
- Sigma CLI — Rule conversion tool
- SIEM access (Splunk, Elastic, QRadar, Microsoft Sentinel)
Core Capabilities
1. IOC Extraction & Normalization
When the user provides a threat report, article, email, or log snippet:
Claude performs these extraction steps:
- Parse all text for indicators using pattern matching:
| IOC Type | Pattern Examples |
|---|
| IPv4 | 192.0.2.1, defanged: 192[.]0[.]2[.]1 |
| IPv6 | 2001:db8::1 |
| Domain | evil.example.com, evil[.]example[.]com |
| URL | hxxp://evil.com/path, https://malicious[.]io/c2 |
| Email | attacker@evil.com, phish[at]evil.com |
| MD5 | 32 hex chars |
| SHA1 | 40 hex chars |
| SHA256 | 64 hex chars |
| CVE | CVE-2024-XXXXX |
| ATT&CK ID | T1059.001, TA0001 |
| Registry Key | HKCU\Software\... |
| File path | C:\Windows\Temp\..., /tmp/... |
| Mutex | Named mutex patterns |
-
Defang extracted indicators — refang before use:
hxxp:// → http://[.] → .[at] → @[:] → :
-
Categorize by type: Network / File / Host / Identity / Vulnerability
-
Score by confidence: High (specific, sourced), Medium (inferred), Low (generic)
-
Output in multiple formats:
python scripts/ioc_extractor.py --input threat_report.txt --output iocs.json
python scripts/ioc_extractor.py --input report.pdf --format stix --output iocs.stix.json
python scripts/ioc_extractor.py --input email.eml --defang --output iocs.csv
STIX 2.1 output template:
{
"type": "indicator",
"id": "indicator--[uuid]",
"created": "2025-05-28T00:00:00.000Z",
"name": "Malicious IP — C2 Infrastructure",
"pattern": "[ipv4-addr:value = '192.0.2.10']",
"pattern_type": "stix",
"valid_from": "2025-05-28T00:00:00Z",
"labels": ["malicious-activity", "c2"],
"confidence": 85
}
2. MITRE ATT&CK Mapping
When the user provides TTPs, behaviors, or a malware report:
python scripts/mitre_mapper.py --input techniques.txt --output attack_map.json
python scripts/mitre_mapper.py --technique T1059.001 --detection-query splunk
Mapping process:
- Analyze each behavior against ATT&CK technique descriptions
- Map to specific Tactic → Technique → Sub-technique (T1059 → T1059.001)
- Assign confidence level based on evidence quality
ATT&CK Tactics Reference:
| Tactic | ID | Description |
|---|
| Reconnaissance | TA0043 | Pre-attack information gathering |
| Resource Development | TA0042 | Establishing attack resources |
| Initial Access | TA0001 | Entry into target environment |
| Execution | TA0002 | Running malicious code |
| Persistence | TA0003 | Maintaining foothold |
| Privilege Escalation | TA0004 | Gaining higher permissions |
| Defense Evasion | TA0005 | Avoiding detection |
| Credential Access | TA0006 | Stealing credentials |
| Discovery | TA0007 | Understanding environment |
| Lateral Movement | TA0008 | Moving through network |
| Collection | TA0009 | Gathering data of interest |
| Command & Control | TA0011 | Communicating with compromised hosts |
| Exfiltration | TA0010 | Stealing data |
| Impact | TA0040 | Disrupting/destroying systems |
ATT&CK Navigator Layer format (JSON for visualization):
{
"name": "Threat Hunt Layer — [Threat Actor/Campaign]",
"versions": {"attack": "14", "navigator": "4.9"},
"domain": "enterprise-attack",
"techniques": [
{
"techniqueID": "T1059.001",
"color": "#ff6666",
"comment": "Observed PowerShell download cradle",
"enabled": true,
"score": 100
}
]
}
3. Hunt Hypothesis Generation
When the user asks for hunt hypotheses:
Use this structured hypothesis template:
## Hunt Hypothesis — [ID]: [Short Name]
**Hypothesis Statement:**
"We believe [Threat Actor/TTPs] may be present in [Environment] based on
[Threat Intelligence / Recent Incidents / Industry Reports]."
**Rationale:**
[Why this threat is relevant to this organization — industry, exposure, recent news]
**ATT&CK Techniques Covered:**
- T1059.001 — PowerShell
- T1053.005 — Scheduled Task/Job
- T1021.001 — Remote Services: Remote Desktop Protocol
**Data Sources Required:**
- Windows Event Logs (Security, System, PowerShell/4104)
- EDR process execution telemetry
- DNS query logs
- Proxy/firewall logs
**Detection Logic:**
[SIEM query or pseudocode]
**Success Criteria:**
- POSITIVE: We find evidence of the technique → escalate to IR (Skill 07)
- NEGATIVE: No evidence after thorough search → document as cleared hunt
- INCONCLUSIVE: Insufficient data → identify logging gaps
**Estimated Hunt Duration:** [X hours]
**Priority:** [High / Medium / Low]
**Analyst:** [Name]
4. SIEM Detection Query Library
When the user asks to build detection queries for specific techniques:
Splunk SPL Queries
// T1059.001 — PowerShell Execution with suspicious flags
index=windows (source="WinEventLog:Microsoft-Windows-PowerShell/Operational" EventCode=4104)
| search ScriptBlockText IN ("*DownloadString*", "*IEX*", "*EncodedCommand*", "*bypass*", "*WebClient*")
| stats count by ComputerName, UserName, ScriptBlockText
| where count > 0
// T1003.001 — LSASS Memory Dump
index=windows EventCode=10 TargetImage="*lsass.exe"
| where NOT (SourceImage IN ("C:\\Windows\\System32\\*", "C:\\Program Files\\*"))
| table _time, SourceImage, TargetImage, GrantedAccess, CallTrace
// T1547.001 — Registry Run Key Persistence
index=windows EventCode=13 TargetObject IN ("*\\Run\\*", "*\\RunOnce\\*")
| where NOT (Image IN ("C:\\Windows\\System32\\*", "C:\\Windows\\SysWOW64\\*"))
| table _time, ComputerName, Image, TargetObject, Details
// T1021.002 — Lateral Movement via SMB Admin Shares
index=windows EventCode=5140
| where ShareName IN ("\\\\*\\ADMIN$", "\\\\*\\C$", "\\\\*\\IPC$")
| stats count by SubjectUserName, IpAddress, ShareName, ObjectType
| where count > 3
Microsoft Sentinel KQL
// T1110.001 — Brute Force Login Attempt
SecurityEvent
| where EventID == 4625
| where TimeGenerated > ago(1h)
| summarize FailCount=count() by TargetAccount, IpAddress=replace(@"\.", "[.]", tostring(parse_json(EventData).IpAddress))
| where FailCount > 20
| join kind=leftouter (
SecurityEvent | where EventID == 4624
| summarize SuccessCount=count() by TargetAccount
) on TargetAccount
| project TargetAccount, IpAddress, FailCount, SuccessCount
| where isnotnull(SuccessCount) // Brute force succeeded!
// T1190 — Exploit Public-Facing Application
AzureDiagnostics
| where Category == "ApplicationGatewayFirewallLog"
| where action_s == "Blocked"
| where ruleSetVersion_s startswith "3."
| summarize count() by clientIp_s, requestUri_s, ruleId_s
| where count_ > 100
| order by count_ desc
Elastic EQL
// T1055 — Process Injection
sequence by host.name
[process where process.name : "notepad.exe" and event.type == "start"]
[process where event.type == "start" and process.parent.name : "notepad.exe"
and not process.name in ("conhost.exe")]
// T1566.001 — Spearphishing with attachment
sequence by user.name within 5m
[file where file.extension in ("doc", "xls", "pdf") and
process.name : ("outlook.exe", "WINWORD.EXE")]
[process where process.name : ("cmd.exe", "powershell.exe", "wscript.exe", "cscript.exe")]
Sigma Rule Template
title: Suspicious PowerShell Download Cradle
id: a3c2f1b4-8e9d-4a2c-b7f6-1234567890ab
status: stable
description: Detects PowerShell commands used to download and execute code from the internet
author: Threat Hunter
date: 2025/05/28
modified: 2025/05/28
references:
- https://attack.mitre.org/techniques/T1059/001/
tags:
- attack.execution
- attack.t1059.001
- attack.defense_evasion
- attack.t1027
logsource:
category: ps_script
product: windows
definition: Script Block Logging enabled (EventID 4104)
detection:
selection:
ScriptBlockText|contains|all:
- 'DownloadString'
- 'IEX'
selection2:
ScriptBlockText|contains:
- '-EncodedCommand'
- '-enc '
- '-WindowStyle Hidden'
- 'Net.WebClient'
- 'WebProxy'
condition: selection or selection2
falsepositives:
- Legitimate software installations
- Administrative scripts
level: high
5. Threat Intelligence Correlation
When the user asks to correlate IOCs or identify threat actors:
-
Cross-reference infrastructure across known campaigns:
- Same registrar + similar registration dates → likely related infrastructure
- IP hosting multiple C2 domains → infrastructure cluster
- Certificate SAN fields → reveal connected domains
-
Map to threat actor groups:
-
Generate Threat Assessment:
## Threat Assessment — [Campaign Name]
**Threat Actor:** [APT Group / Criminal Group / Unknown]
**Confidence:** [High / Medium / Low]
**Motivation:** [Espionage / Financial / Hacktivism]
**Targeting:** [Industries / Countries / Organization types]
**Campaign IOCs:**
- Infrastructure: [IPs, domains]
- Malware: [Family names, hashes]
- TTPs: [ATT&CK technique IDs]
**Relevance to Organization:**
[Why this threat is or isn't relevant]
**Recommended Actions:**
1. Block IOCs in firewall/proxy
2. Hunt for T1XXX in SIEM
3. Deploy YARA rules for detection
Script Reference
ioc_extractor.py
python scripts/ioc_extractor.py --input threat_report.txt --output iocs.json
python scripts/ioc_extractor.py --input report.pdf --format stix --output iocs.stix.json
python scripts/ioc_extractor.py --input email.eml --defang --output iocs.csv
mitre_mapper.py
python scripts/mitre_mapper.py --input techniques.txt --output attack_map.json
python scripts/mitre_mapper.py --technique T1059.001 --detection-query splunk
python scripts/mitre_mapper.py --actor "APT29" --output apt29_layer.json
Skill Integration
| Condition | Adjacent Skill |
|---|
| IOCs from malware samples | ← Skill 05 (Malware Analysis) |
| IOCs from IR engagement | ← Skill 07 (Incident Response) |
| Feed hunting queries to SIEM | → Skill 12 (Log Analysis) |
| Generate detection rules | → Skill 15 (Blue Team Defense) |
| Automate response to findings | → Skill 11 (CSOC Automation) |
References
v3.0 Enhancements (2026 Update)
Threat-informed, repeatable hunting:
- ATT&CK current version — map to the latest Enterprise matrix (incl. updated cloud, identity, and containers techniques); call out sub-techniques explicitly.
- PEAK hunting framework — structure hunts as Prepare → Execute → Act with a documented hypothesis, data sources, and ABLE (Actor/Behavior/Location/Evidence) baselining.
- Identity-centric hunting — Entra ID / Okta logs: impossible travel, illicit OAuth consent grants, MFA-fatigue, token theft & replay, and risky sign-in correlation.
- Living-off-the-land — baseline LOLBin/LOLBAS usage and hunt deviations rather than static signatures.
- Detection-as-code — express hunt findings as Sigma rules under version control with test data, then promote validated hunts into Skill 12/15 detections.
- Hunt maturity — track from ad-hoc → data-driven → automated; record which hunts became scheduled detections.
Precision rule: every hunt yields a hypothesis, the query, the result (found/not-found/inconclusive), and a disposition (new detection / tuned alert / closed).
