Detection Engineer

Transform malware analysis findings into production-ready detection rules, hunting queries, and operationalized IOCs.

Note: YARA rules are authored in the malware-report-writer skill, not here. This skill covers Sigma rules, Suricata/Snort rules, and hunting queries.

Execution Model

• Start from the evidence, not from the user's memory. Read analysis_state.md, procmon_summary.txt, sysmon_summary.txt, and the tshark exports yourself; every rule below is derived from a specific observed behavior or network artifact, and you cite it in the rule's description/reference.
• Locate skill files. Scripts and reference files ship in this skill's directory. Set R="${CLAUDE_PLUGIN_ROOT:-<dir containing this SKILL.md>}" once (when installed as a plugin $CLAUDE_PLUGIN_ROOT is set; otherwise it is this skill folder). Your working directory is the user's analysis workspace, so prefix every script path below with $R, e.g. python3 "$R"/scripts/ioc_extract.py.
• Write rules to files: detections/sigma/<name>.yml, detections/suricata/<name>.rules, detections/hunting/<platform>.txt, detections/iocs.csv|.json. Create the directories.
• Test what you can, say what you couldn't. Run sigma check and suricata -T (below) when installed; otherwise write status: experimental and note "untested" in the state file. Never claim a rule is validated without output to show.
• Defang with the bundled script: python3 scripts/ioc_extract.py <evidence files> (repo root) produces the deduplicated, defanged list; --format csv|json feeds the export formats; --refang restores live values for rule bodies.
• UUIDs: python3 -c "import uuid; print(uuid.uuid4())" per Sigma rule. SIDs: 1000000+ and unique across the engagement.
• Ask the user only for: target SIEM/EDR platforms, deployment constraints (noise tolerance, log sources actually collected), and sharing scope (TLP).

When to Use This Skill

Use this skill when you need to:

• Write Sigma rules for SIEM detection (Splunk, Elastic, QRadar)
• Create Suricata/Snort rules for network IDS/IPS
• Generate hunting queries for EDR platforms
• Defang IOCs for safe documentation and sharing
• Convert IOCs to standard formats (STIX, OpenIOC, CSV)
• Assess IOC confidence levels and volatility
• Create detection logic from behavioral analysis
• Write threat hunting hypotheses

IOC Management & Defanging

Why Defang IOCs?

Problem: Live IOCs in reports can be:

• Accidentally clicked (execute malware)
• Automatically crawled by bots
• Trigger security tools (email filters, DLP)

Solution: Defang (neutralize) IOCs for safe sharing.

Defanging Patterns

# URLs
http://malicious.com/payload.exe
→ hxxp://malicious[.]com/payload[.]exe

https://evil.tk/login
→ hxxps://evil[.]tk/login

# Domains
malicious.com
→ malicious[.]com

c2-server.example.org
→ c2-server[.]example[.]org

# IPs
192.168.1.100
→ 192[.]168[.]1[.]100

10.0.0.50
→ 10[.]0[.]0[.]50

# Email addresses
attacker@evil.com
→ attacker[@]evil[.]com

phishing@malware.tk
→ phishing[@]malware[.]tk

# File paths (optional)
C:\Windows\System32\malware.exe
→ C:\Windows\System32\malware[.]exe

Automated Defanging

Bundled script (preferred — no install):

python3 scripts/ioc_extract.py evidence/*.txt evidence/*_summary.txt          # defanged, deduplicated, typed
python3 scripts/ioc_extract.py --format csv evidence/*.txt > detections/iocs.csv
python3 scripts/ioc_extract.py --refang detections/iocs_defanged.txt          # live values for Suricata/Sigma bodies

Tool: ioc-fanger (Python)

# Install
pip install ioc-fanger

# Defang
echo "http://malicious.com" | fanger --defang
# Output: hxxp://malicious[.]com

# Refang (restore for testing)
echo "hxxp://malicious[.]com" | fanger --fang
# Output: http://malicious.com

Manual sed/awk:

# Defang URLs and domains
echo "http://malicious.com/payload.exe" | sed 's/http:/hxxp:/g; s/\./[.]/g'

# Defang IPs
echo "192.168.1.100" | sed 's/\./[.]/g'

# Defang emails
echo "attacker@evil.com" | sed 's/@/[@]/g; s/\./[.]/g'

IOC Confidence & Volatility Assessment

IOC Type	Confidence	Volatility	Reasoning
File Hash (SHA256)	High	Static	Unique to sample, won't change
Mutex Name	High	Static	Hardcoded in malware
PDB Path	High	Static	Compilation artifact
Registry Key	High	Static	Persistence mechanism
Certificate Hash	High	Static	Code signing certificate
IP Address	Medium	Dynamic	Can change (DGA, fast-flux, hosting)
Domain (C2)	Medium	Dynamic	May rotate frequently
URL Path	Low-Medium	Dynamic	Often dynamic or timestamped
User-Agent	Low	Dynamic	Common strings, high FP rate
File Path	Medium	Static	May vary by environment
Process Name	Low	Dynamic	Easily changed by attacker

Label IOCs appropriately:

### Network Indicators (Medium Confidence - Dynamic)
- Domain: malicious[.]com (C2 server - may rotate)
- IP: 192[.]168[.]1[.]100 (C2 IP - may change)

### Host Indicators (High Confidence - Static)
- Mutex: Global\UniqueMalwareMutex
- Registry: HKCU\Software\Microsoft\Windows\CurrentVersion\Run\Malware
- File Hash: abc123... (SHA256)

---

Sigma Rule Creation (SIEM Detection)

What is Sigma?

Sigma is a generic signature format for SIEM systems. Write once, convert to Splunk/Elastic/QRadar/ArcSight queries.

Official Repo: https://github.com/SigmaHQ/sigma

Sigma Rule Structure

title: Short Descriptive Title
id: unique-uuid-for-this-rule
status: experimental | test | stable
description: Detailed description of what this detects
references:
    - https://attack.mitre.org/techniques/T1059/001/
author: Your Name
date: 2025-10-26
tags:
    - attack.execution
    - attack.t1059.001
logsource:
    category: process_creation  # or network_connection, file_event, etc.
    product: windows
detection:
    selection:
        Image|endswith: '\powershell.exe'
        CommandLine|contains|all:
            - 'DownloadString'
            - 'Invoke-Expression'
    condition: selection
falsepositives:
    - Legitimate administrative scripts
level: high  # informational, low, medium, high, critical

Common Sigma Logsources

Category	Product	Event Source	Use Case
process_creation	windows	Sysmon Event ID 1, Security 4688	Process execution
network_connection	windows	Sysmon Event ID 3	Network activity
file_event	windows	Sysmon Event ID 11	File creation
registry_set	windows	Sysmon Event ID 13	Registry value writes (persistence)
registry_add / registry_delete	windows	Sysmon Event ID 12	Key creation / deletion
registry_event	windows	Sysmon Event ID 12/13/14	Generic registry (prefer the specific categories above)
image_load	windows	Sysmon Event ID 7	DLL loading
create_remote_thread	windows	Sysmon Event ID 8	Process injection
dns_query	windows	Sysmon Event ID 22	DNS queries

Sigma Modifiers

String Matching:

• |contains - String contains value
• |startswith - String starts with value
• |endswith - String ends with value
• |all - All values must be present
• |re - Regular expression match

Examples:

# Contains any
CommandLine|contains:
    - 'powershell'
    - 'cmd.exe'

# Contains all
CommandLine|contains|all:
    - 'Invoke-WebRequest'
    - '-OutFile'

# Ends with
Image|endswith: '\rundll32.exe'

# Starts with
CommandLine|startswith: 'C:\Windows\System32\'

# Regex
CommandLine|re: '.*\\\\AppData\\\\Local\\\\Temp\\\\[a-z]{8}\.exe'

Example 1: PowerShell Download Cradle

Generate unique UUID: python3 -c "import uuid; print(uuid.uuid4())"

title: Suspicious PowerShell Download and Execute
id: a6e0ee39-d2cb-477d-9223-7b9e6090613a
status: experimental
description: Detects PowerShell downloading content and executing it via Invoke-Expression
references:
    - https://attack.mitre.org/techniques/T1059/001/
    - https://attack.mitre.org/techniques/T1105/
author: Analyst Name
date: 2025-10-26
tags:
    - attack.execution
    - attack.t1059.001
    - attack.command_and_control
    - attack.t1105
logsource:
    category: process_creation
    product: windows
detection:
    selection:
        Image|endswith:
            - '\powershell.exe'
            - '\pwsh.exe'
        CommandLine|contains|all:
            - 'DownloadString'
            - 'IEX'
    condition: selection
falsepositives:
    - Legitimate software deployment scripts
    - Administrative automation
level: high

Example 2: Suspicious Registry Run Key

title: Malware Persistence via Registry Run Key
id: 8ff9a2f5-f996-4405-a5f1-d79d680cb5e6
status: stable
description: Detects creation of registry Run key pointing to suspicious locations
references:
    - https://attack.mitre.org/techniques/T1547/001/
author: Analyst Name
date: 2025-10-26
tags:
    - attack.persistence
    - attack.t1547.001
logsource:
    category: registry_set
    product: windows
detection:
    selection:
        TargetObject|contains: '\Software\Microsoft\Windows\CurrentVersion\Run\'
        Details|contains:
            - '\AppData\Local\Temp\'
            - '\Users\Public\'
            - '\ProgramData\'
            - '%TEMP%'
    condition: selection
falsepositives:
    - Legitimate software installations
level: medium

Example 3: Network Connection to Malicious IP

title: Network Connection to Known C2 Server
id: 54502807-546d-4152-abd4-7c12ac7f9833
status: experimental
description: Detects network connection to known malware C2 IP address
references:
    - Internal malware analysis report
author: Analyst Name
date: 2025-10-26
tags:
    - attack.command_and_control
    - attack.t1071
logsource:
    category: network_connection
    product: windows
detection:
    selection:
        DestinationIp:
            - '192.168.56.101'  # Replace with actual C2 IP
            - '10.0.0.50'
        DestinationPort:
            - 443
            - 8080
    condition: selection
falsepositives:
    - Rare, should be investigated
level: high

Example 4: Suspicious File Creation

title: Malware Dropping Files to Suspicious Location
id: 37ec0b15-debf-46d3-8132-ff7aa637a0b0
status: experimental
description: Detects file creation in common malware drop locations
references:
    - https://attack.mitre.org/techniques/T1105/
author: Analyst Name
date: 2025-10-26
tags:
    - attack.defense_evasion
    - attack.t1105
logsource:
    category: file_event
    product: windows
detection:
    selection:
        TargetFilename|contains:
            - '\AppData\Local\Temp\'
            - '\Users\Public\'
        TargetFilename|endswith:
            - '.exe'
            - '.dll'
            - '.bat'
            - '.vbs'
    condition: selection
falsepositives:
    - Software installations
    - Temporary file creation by legitimate apps
level: low

Convert Sigma to SIEM Queries

Using sigma-cli (modern — replaces legacy sigmac):

# Install sigma-cli plus the backend plugin(s) you need (backends are not bundled)
pip install sigma-cli
sigma plugin install splunk          # also: elasticsearch, qradar, sentinel (azure-monitor), sqlite ...

# Validate syntax and logsource fields first
sigma check rule.yml

# Convert — pass a processing pipeline matching your log source (Sysmon field names differ from raw 4688)
sigma convert -t splunk -p sysmon rule.yml
sigma convert -t elasticsearch -p ecs_windows rule.yml
sigma convert -t qradar -p sysmon rule.yml
sigma convert -t microsoft365defender rule.yml   # no pipeline needed

# List installed backends / pipelines
sigma list targets
sigma list pipelines splunk

Example Conversions:

Splunk:

index=windows EventCode=1
(Image="*\\powershell.exe" OR Image="*\\pwsh.exe")
CommandLine="*DownloadString*" CommandLine="*IEX*"

Elastic:

{
  "query": {
    "bool": {
      "must": [
        {"wildcard": {"process.executable": "*\\\\powershell.exe"}},
        {"wildcard": {"process.command_line": "*DownloadString*"}},
        {"wildcard": {"process.command_line": "*IEX*"}}
      ]
    }
  }
}

Sigma Rule Best Practices

Do:

• Use unique UUIDs (generate with uuidgen or online)
• Include MITRE ATT&CK tags
• List realistic false positives
• Test on real data before deployment
• Use specific conditions (avoid over-matching)
• Document references and context
• Set appropriate severity levels

Don't:

• Use overly broad conditions
• Forget false positive analysis
• Skip testing
• Hardcode environment-specific values
• Ignore performance impact

---

Suricata Rule Creation (Network IDS)

Suricata Rule Structure

action protocol src_ip src_port -> dest_ip dest_port (rule_options)

Components:

• Action: alert, drop, reject, pass
• Protocol: tcp, udp, icmp, http, dns, tls
• Src/Dest: IP ranges, ports, $variables
• Rule Options: Keywords that define detection logic

Example 1: HTTP C2 Traffic

alert http $HOME_NET any -> $EXTERNAL_NET any (
    msg:"CUSTOM MALWARE Suspicious C2 Checkin";
    flow:established,to_server;
    http.method; content:"POST";
    http.uri; content:"/api/checkin";
    http.user_agent; content:"Mozilla/4.0 (compatible|3b| MSIE 6.0)";
    sid:1000001;
    rev:1;
    metadata:created_at 2025_10_26;
)

Breakdown:

• alert http - Alert on HTTP traffic
• $HOME_NET any -> $EXTERNAL_NET any - Outbound traffic
• flow:established,to_server - Established connection to server
• http.method; content:"POST" - HTTP POST request (sticky buffer)
• http.uri; content:"/api/checkin" - Specific URI path (sticky buffer)
• http.user_agent; content:"..." - Specific User-Agent (sticky buffer)
• sid:1000001 - Signature ID (use 1000000+ for custom rules)
• rev:1 - Revision number

Example 2: DNS C2 Communication

alert dns $HOME_NET any -> any 53 (
    msg:"CUSTOM MALWARE Suspicious DGA Domain Query";
    dns.query; content:".tk"; nocase;
    sid:1000002;
    rev:1;
    metadata:created_at 2025_10_26;
)

Example 3: TLS C2 with SNI

alert tls $HOME_NET any -> $EXTERNAL_NET 443 (
    msg:"CUSTOM MALWARE Known C2 Server Certificate";
    tls.sni; content:"malicious.com";
    tls.cert_subject; content:"CN=Evil Corp";
    sid:1000003;
    rev:1;
    metadata:created_at 2025_10_26;
)

Example 4: Malware Download

alert http $HOME_NET any -> $EXTERNAL_NET any (
    msg:"CUSTOM MALWARE Executable Download from Suspicious TLD";
    flow:established,to_server;
    http.uri; content:".exe"; endswith;
    http.host; content:".tk"; endswith;
    sid:1000004;
    rev:1;
    metadata:created_at 2025_10_26;
)

Suricata HTTP Keywords

• http.method - GET, POST, PUT, etc.
• http.uri - Request URI path
• http.host - Host header
• http.user_agent - User-Agent string
• http.request_body - POST data
• http.response_body - Response content
• http.header - Any HTTP header
• http.stat_code - Response code (200, 404, etc.)

Suricata DNS Keywords

• dns.query - DNS query name
• dns.opcode - DNS operation code
• dns.rcode - DNS response code

Suricata TLS Keywords

• tls.sni - Server Name Indication
• tls.cert_subject - Certificate subject
• tls.cert_issuer - Certificate issuer
• tls.cert_serial - Certificate serial number
• tls.version - TLS version

Testing Suricata Rules

# Test rule syntax
suricata -T -c /etc/suricata/suricata.yaml -S custom.rules

# Run on PCAP
suricata -r sample_traffic.pcapng -S custom.rules -l /var/log/suricata/

# Check alerts
cat /var/log/suricata/fast.log

Suricata Best Practices

Do:

• Use flow keywords (established, to_server, to_client)
• Anchor strings with content modifiers (startswith, endswith)
• Use fast_pattern for performance
• Test against PCAPs before deployment
• Use metadata for rule management
• Include revision tracking

Don't:

• Write overly broad rules (high false positive rate)
• Use regex unless necessary (performance impact)
• Forget to test on benign traffic
• Use conflicting SIDs (must be unique)
• Skip documentation in msg field

---

Hunting Queries

Splunk Hunting Queries

Hunt for PowerShell Download Cradles:

index=windows EventCode=1
(Image="*\\powershell.exe" OR Image="*\\pwsh.exe")
(CommandLine="*DownloadString*" OR CommandLine="*DownloadFile*" OR CommandLine="*Invoke-WebRequest*")
| table _time, ComputerName, User, CommandLine
| sort -_time

Hunt for Suspicious Registry Run Keys:

index=windows EventCode=13
TargetObject="*\\Software\\Microsoft\\Windows\\CurrentVersion\\Run*"
(Details="*\\AppData\\Local\\Temp\\*" OR Details="*\\Users\\Public\\*" OR Details="*\\ProgramData\\*")
| table _time, ComputerName, TargetObject, Details
| sort -_time

Hunt for Outbound Connections to Rare Destinations:

index=network
| stats count by dest_ip
| where count < 5
| join dest_ip [search index=network]
| table _time, src_ip, dest_ip, dest_port, bytes_out

Elastic (KQL) Hunting Queries

Hunt for Process Injection:

event.code:8 AND
winlog.event_data.TargetImage:(*\\explorer.exe OR *\\svchost.exe) AND
NOT winlog.event_data.SourceImage:C\\:\\Windows\\System32\\*

Hunt for Suspicious File Creations:

event.code:11 AND
file.path:(*\\AppData\\Local\\Temp\\*.exe OR *\\Users\\Public\\*.exe) AND
NOT process.executable:(*\\Windows\\System32\\* OR *\\Program Files\\*)

EDR Hunting (Generic Pseudocode)

Hunt for Credential Access:

Process = "lsass.exe" AND
AccessMask IN (0x1010, 0x1410, 0x1438) AND
SourceImage NOT IN (known_good_processes)

Hunt for Lateral Movement:

Process = "psexec.exe" OR
Process = "wmic.exe" OR
(Process = "powershell.exe" AND CommandLine CONTAINS "Invoke-Command")

---

IOC Formats & Standards

STIX (Structured Threat Information Expression)

STIX 2.1 Example:

{
  "type": "indicator",
  "spec_version": "2.1",
  "id": "indicator--91ec6a8e-61ab-4c6a-b8f5-95240110b203",
  "created": "2025-10-26T12:00:00.000Z",
  "modified": "2025-10-26T12:00:00.000Z",
  "name": "Malicious Domain: malicious.com",
  "description": "C2 domain for Malware Family X",
  "pattern": "[domain-name:value = 'malicious.com']",
  "pattern_type": "stix",
  "valid_from": "2025-10-26T12:00:00.000Z",
  "labels": ["malicious-activity"]
}

CSV Format (Simple)

ioc_type,ioc_value,confidence,description,first_seen
domain,malicious.com,high,C2 server,2025-10-26
ip,192.168.1.100,medium,C2 IP address,2025-10-26
sha256,abc123...,high,Malware sample hash,2025-10-26
mutex,Global\M12345,high,Mutex name,2025-10-26
registry,HKCU\Software\...\Run,high,Persistence key,2025-10-26

OpenIOC Format

<?xml version="1.0" encoding="UTF-8"?>
<ioc xmlns="http://schemas.mandiant.com/2010/ioc">
  <short_description>Malware Family X IOCs</short_description>
  <description>IOCs from analysis of Malware Family X</description>
  <authored_by>Analyst Name</authored_by>
  <authored_date>2025-10-26T12:00:00</authored_date>
  <definition>
    <Indicator operator="OR">
      <IndicatorItem>
        <Context document="FileItem" search="FileItem/Md5sum"/>
        <Content type="md5">abc123...</Content>
      </IndicatorItem>
      <IndicatorItem>
        <Context document="Network" search="Network/DNS"/>
        <Content type="string">malicious.com</Content>
      </IndicatorItem>
    </Indicator>
  </definition>
</ioc>

---

Detection Logic Development

From Analysis to Detection

Step 1: Identify Unique Behaviors From dynamic analysis, extract behaviors that are:

• Uncommon in legitimate software
• Hard for attackers to change
• Observable in logs/network traffic

Step 2: Map to Data Sources

Behavior	Data Source	Detection Method
Process injection	Sysmon Event ID 8	Sigma rule
C2 beacon	Network logs, proxy	Suricata rule
Registry persistence	Sysmon Event ID 13	Sigma rule
File drop	Sysmon Event ID 11	Sigma rule + YARA
DNS query (DGA)	DNS logs	Suricata rule

Step 3: Write Detection Rule

Choose appropriate rule type:

• Host-based → Sigma rule (SIEM/EDR)
• Network-based → Suricata rule (IDS/IPS)
• File-based → YARA rule (scanning)

Step 4: Test & Validate

• Test on malware sample (must alert)
• Test on benign samples (must not alert)
• Adjust thresholds/conditions
• Document false positive scenarios

Step 5: Deploy & Tune

• Deploy to pilot environment
• Monitor alert volume
• Investigate false positives
• Tune rule based on feedback
• Document tuning changes

---

Quality Checklist

Before finalizing detection content:

Sigma Rules:

• Unique UUID assigned
• MITRE ATT&CK tags included
• Tested on sample data
• False positives documented
• Appropriate severity level set
• References included
• Logsource correctly specified

Suricata Rules:

• Unique SID assigned (1000000+)
• Tested on PCAP
• Flow keywords used (performance)
• Metadata included
• No syntax errors (suricata -T)
• Tested on benign traffic
• Message clearly describes detection

IOCs:

• All IOCs defanged properly
• Confidence levels assigned
• Volatility assessed
• Context provided for each IOC
• No environment-specific artifacts
• Timestamps included (UTC)
• Format standardized (CSV/STIX/OpenIOC)

Hunting Queries:

• Query tested and returns results
• Performance acceptable (<30s)
• Results actionable
• False positive rate acceptable
• Query documented (purpose, expected results)

---

Integration with Malware Reports

Detection content appears in multiple report sections:

IOCs Section:

• Defanged IOCs grouped by type
• Confidence ratings
• Context for each indicator

Detection Rules Section:

• YARA rules (from malware-report-writer skill)
• Sigma rules (from this skill)
• Suricata rules (from this skill)

Remediation Section:

• Hunting queries for IR teams
• Detection deployment guidance
• IOC search instructions

Appendix:

• IOC export files (CSV, STIX)
• Sigma rule files (.yml)
• Suricata rule files (.rules)

---

Tool Quick Reference

Task	Tool	Command
Defang IOCs	ioc-fanger	echo "http://evil.com" | fanger --defang
Convert Sigma	sigma-cli	sigma convert -t splunk rule.yml
Test Suricata	suricata	suricata -T -S rules.rules
Generate UUID	uuidgen	uuidgen (Linux/Mac) or online
Validate STIX	stix2-validator	stix2_validator file.json

---

Example Usage

User request: "Create detection rules for the ransomware"

What you do:

1. Read analysis_state.md and the dynamic summaries; list the detectable behaviors (vssadmin shadow deletion, mass rename to .locked, ransom-note drop, Run key, C2 POST with fixed UA/URI).
2. Write one Sigma rule per behavior (process_creation, file_event, registry_set) with ATT&CK tags and a real UUID; sigma check them.
3. Write Suricata rules for the C2 HTTP pattern and DNS name with SIDs ≥ 1000000; suricata -T them.
4. Write Splunk/KQL hunting queries for the same behaviors.
5. ioc_extract.py --format csv → detections/iocs.csv; STIX if requested.
6. Record rule paths + test results in analysis_state.md; recommend malware-report-writer next.