claude-skills-reference/engineering-team/threat-detection/SKILL.md

name, description

name	description
threat-detection	Use when hunting for threats in an environment, analyzing IOCs, or detecting behavioral anomalies in telemetry. Covers hypothesis-driven threat hunting, IOC sweep generation, z-score anomaly detection, and MITRE ATT&CK-mapped signal prioritization.

Threat Detection

Threat detection skill for proactive discovery of attacker activity through hypothesis-driven hunting, IOC analysis, and behavioral anomaly detection. This is NOT incident response (see incident-response) or red team operations (see red-team) — this is about finding threats that have evaded automated controls.

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Table of Contents

• Overview
• Threat Signal Analyzer
• Threat Hunting Methodology
• IOC Analysis
• Anomaly Detection
• MITRE ATT&CK Signal Prioritization
• Deception and Honeypot Integration
• Workflows
• Anti-Patterns
• Cross-References

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Overview

What This Skill Does

This skill provides the methodology and tooling for proactive threat detection — finding attacker activity through structured hunting hypotheses, IOC analysis, and statistical anomaly detection before alerts fire.

Distinction from Other Security Skills

Skill	Focus	Approach
threat-detection (this)	Finding hidden threats	Proactive — hunt before alerts
incident-response	Active incidents	Reactive — contain and investigate declared incidents
red-team	Offensive simulation	Offensive — test defenses from attacker perspective
cloud-security	Cloud misconfigurations	Posture — IAM, S3, network exposure

Prerequisites

Read access to SIEM/EDR telemetry, endpoint logs, and network flow data. IOC feeds require freshness within 30 days to avoid false positives. Hunting hypotheses must be scoped to the environment before execution.

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Threat Signal Analyzer

The threat_signal_analyzer.py tool supports three modes: hunt (hypothesis scoring), ioc (sweep generation), and anomaly (statistical detection).

# Hunt mode: score a hypothesis against MITRE ATT&CK coverage
python3 scripts/threat_signal_analyzer.py --mode hunt \
  --hypothesis "Lateral movement via PtH using compromised service account" \
  --actor-relevance 3 --control-gap 2 --data-availability 2 --json

# IOC mode: generate sweep targets from an IOC feed file
python3 scripts/threat_signal_analyzer.py --mode ioc \
  --ioc-file iocs.json --json

# Anomaly mode: detect statistical outliers in telemetry events
python3 scripts/threat_signal_analyzer.py --mode anomaly \
  --events-file telemetry.json \
  --baseline-mean 100 --baseline-std 25 --json

# List all supported MITRE ATT&CK techniques
python3 scripts/threat_signal_analyzer.py --list-techniques

IOC file format

{
  "ips": ["1.2.3.4", "5.6.7.8"],
  "domains": ["malicious.example.com"],
  "hashes": ["abc123def456..."]
}

Telemetry events file format

[
  {"timestamp": "2024-01-15T14:32:00Z", "entity": "host-01", "action": "dns_query", "volume": 450},
  {"timestamp": "2024-01-15T14:33:00Z", "entity": "host-02", "action": "dns_query", "volume": 95}
]

Exit codes

Code	Meaning
0	No high-priority findings
1	Medium-priority signals detected
2	High-priority confirmed findings

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Threat Hunting Methodology

Structured threat hunting follows a five-step loop: hypothesis → data source identification → query execution → finding triage → feedback to detection engineering.

Hypothesis Scoring

Factor	Weight	Description
Actor relevance	×3	How closely does this TTP match known threat actors in your sector?
Control gap	×2	How many of your existing controls would miss this behavior?
Data availability	×1	Do you have the telemetry data needed to test this hypothesis?

Priority score = (actor_relevance × 3) + (control_gap × 2) + (data_availability × 1)

High-Value Hunt Hypotheses by Tactic

Hypothesis	MITRE ID	Data Sources	Priority Signal
WMI lateral movement via remote execution	T1047	WMI logs, EDR process telemetry	WMI process spawned from WINRM, unusual parent-child chain
LOLBin execution for defense evasion	T1218	Process creation, command-line args	certutil.exe, regsvr32.exe, mshta.exe with network activity
Beaconing C2 via jitter-heavy intervals	T1071.001	Proxy logs, DNS logs	Regular interval outbound connections ±10% jitter
Pass-the-Hash lateral movement	T1550.002	Windows security event 4624 type 3	NTLM auth from unexpected source host to admin share
LSASS memory access	T1003.001	EDR memory access events	OpenProcess on lsass.exe from non-system process
Kerberoasting	T1558.003	Windows event 4769	High volume TGS requests for service accounts
Scheduled task persistence	T1053.005	Sysmon Event 1/11, Windows 4698	Scheduled task created in non-standard directory

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IOC Analysis

IOC analysis determines whether indicators are fresh, maps them to required sweep targets, and filters stale data that generates false positives.

IOC Types and Sweep Priority

IOC Type	Staleness Threshold	Sweep Target	MITRE Coverage
IP addresses	30 days	Firewall logs, NetFlow, proxy logs	T1071, T1105
Domains	30 days	DNS resolver logs, proxy logs	T1568, T1583
File hashes	90 days	EDR file creation, AV scan logs	T1105, T1027
URLs	14 days	Proxy access logs, browser history	T1566.002
Mutex names	180 days	EDR runtime artifacts	T1055

IOC Staleness Handling

IOCs older than their threshold are flagged as stale and excluded from sweep target generation. Running sweeps against stale IOCs inflates false positive rates and reduces SOC credibility. Refresh IOC feeds from threat intelligence platforms (MISP, OpenCTI, commercial TI) before every hunt cycle.

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Anomaly Detection

Statistical anomaly detection identifies behavior that deviates from established baselines without relying on known-bad signatures.

Z-Score Thresholds

Z-Score	Classification	Response
< 2.0	Normal	No action required
2.0–2.9	Soft anomaly	Log and monitor — increase sampling
≥ 3.0	Hard anomaly	Escalate to hunt analyst — investigate entity

Baseline Requirements

Effective anomaly detection requires at least 14 days of historical telemetry to establish a valid baseline. Baselines must be recomputed after:

• Security incidents (post-incident behavior change)
• Major infrastructure changes (cloud migrations, new SaaS deployments)
• Seasonal usage pattern changes (end of quarter, holiday periods)

High-Value Anomaly Targets

Entity Type	Metric	Anomaly Indicator
DNS resolver	Queries per hour per host	Beaconing, tunneling, DGA
Endpoint	Unique process executions per day	Malware installation, LOLBin abuse
Service account	Auth events per hour	Credential stuffing, lateral movement
Email gateway	Attachment types per hour	Phishing campaign spike
Cloud IAM	API calls per identity per hour	Credential compromise, exfiltration

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MITRE ATT&CK Signal Prioritization

Each hunting hypothesis maps to one or more ATT&CK techniques. Techniques with multiple confirmed signals in your environment are higher priority.

Tactic Coverage Matrix

Tactic	Key Techniques	Primary Data Source
Initial Access	T1190, T1566, T1078	Web access logs, email gateway, auth logs
Execution	T1059, T1047, T1218	Process creation, command-line, script execution
Persistence	T1053, T1543, T1098	Scheduled tasks, services, account changes
Defense Evasion	T1027, T1562, T1070	Process hollowing, log clearing, encoding
Credential Access	T1003, T1558, T1110	LSASS, Kerberos, auth failures
Lateral Movement	T1550, T1021, T1534	NTLM auth, remote services, internal spearphish
Collection	T1074, T1560, T1114	Staging directories, archive creation, email access
Exfiltration	T1048, T1041, T1567	Unusual outbound volume, DNS tunneling, cloud storage
Command & Control	T1071, T1572, T1568	Beaconing, protocol tunneling, DNS C2

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Deception and Honeypot Integration

Deception assets generate high-fidelity alerts — any interaction with a honeypot is an unambiguous signal requiring investigation.

Deception Asset Types and Placement

Asset Type	Placement	Signal	ATT&CK Technique
Honeypot credentials in password vault	Vault secrets store	Credential access attempt	T1555
Honey tokens (fake AWS access keys)	Git repos, S3 objects	Reconnaissance or exfiltration	T1552.004
Honey files (named: passwords.xlsx)	File shares, endpoints	Collection staging	T1074
Honey accounts (dormant AD users)	Active Directory	Lateral movement pivot	T1078.002
Honeypot network services	DMZ, flat network segments	Network scanning, service exploitation	T1046, T1190

Honeypot alerts bypass the standard scoring pipeline — any hit is an automatic SEV2 until proven otherwise.

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Workflows

Workflow 1: Quick Hunt (30 Minutes)

For responding to a new threat intelligence report or CVE alert:

# 1. Score hypothesis against environment context
python3 scripts/threat_signal_analyzer.py --mode hunt \
  --hypothesis "Exploitation of CVE-YYYY-NNNNN in Apache" \
  --actor-relevance 2 --control-gap 3 --data-availability 2 --json

# 2. Build IOC sweep list from threat intel
echo '{"ips": ["1.2.3.4"], "domains": ["malicious.tld"], "hashes": []}' > iocs.json
python3 scripts/threat_signal_analyzer.py --mode ioc --ioc-file iocs.json --json

# 3. Check for anomalies in web server telemetry from last 24h
python3 scripts/threat_signal_analyzer.py --mode anomaly \
  --events-file web_events_24h.json --baseline-mean 80 --baseline-std 20 --json

Decision: If hunt priority ≥ 7 or any IOC sweep hits, escalate to full hunt.

Workflow 2: Full Threat Hunt (Multi-Day)

Day 1 — Hypothesis Generation:

1. Review threat intelligence feeds for sector-relevant TTPs
2. Map last 30 days of security alerts to ATT&CK tactics to identify gaps
3. Score top 5 hypotheses with threat_signal_analyzer.py hunt mode
4. Prioritize by score — start with highest

Day 2 — Data Collection and Query Execution:

1. Pull relevant telemetry from SIEM (date range: last 14 days)
2. Run anomaly detection across entity baselines
3. Execute IOC sweeps for all feeds fresh within 30 days
4. Review hunt playbooks in references/hunt-playbooks.md

Day 3 — Triage and Reporting:

1. Triage all anomaly findings — confirm or dismiss
2. Escalate confirmed activity to incident-response
3. Document new detection rules from hunt findings
4. Submit false-positive IOCs back to TI provider

Workflow 3: Continuous Monitoring (Automated)

Configure recurring anomaly detection against key entity baselines on a 6-hour cadence:

# Run as cron job every 6 hours — auto-escalate on exit code 2
python3 scripts/threat_signal_analyzer.py --mode anomaly \
  --events-file /var/log/telemetry/events_6h.json \
  --baseline-mean "${BASELINE_MEAN}" \
  --baseline-std "${BASELINE_STD}" \
  --json > /var/log/threat-detection/$(date +%Y%m%d_%H%M%S).json

# Alert on exit code 2 (hard anomaly)
if [ $? -eq 2 ]; then
  send_alert "Hard anomaly detected — threat_signal_analyzer"
fi

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Anti-Patterns

1. Hunting without a hypothesis — Running broad queries across all telemetry without a focused question generates noise, not signal. Every hunt must start with a testable hypothesis scoped to one or two ATT&CK techniques.
2. Using stale IOCs — IOCs older than 30 days generate false positives that train analysts to ignore alerts. Always check IOC freshness before sweeping; exclude stale indicators from automated sweeps.
3. Skipping baseline establishment — Anomaly detection without a valid baseline produces alerts on normal high-volume days. Require 14+ days of baseline data before enabling statistical alerting on any entity type.
4. Hunting only known techniques — Hunting exclusively against documented ATT&CK techniques misses novel adversary behavior. Regularly include open-ended anomaly analysis that can surface unknown TTPs.
5. Not closing the feedback loop to detection engineering — Hunt findings that confirm malicious behavior must produce new detection rules. Hunting that doesn't improve detection coverage has no lasting value.
6. Treating every anomaly as a confirmed threat — High z-scores indicate deviation from baseline, not confirmed malice. All anomalies require human triage to confirm or dismiss before escalation.
7. Ignoring honeypot alerts — Any interaction with a deception asset is a high-fidelity signal. Treating honeypot alerts as noise invalidates the entire deception investment.

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Cross-References

Skill	Relationship
incident-response	Confirmed threats from hunting escalate to incident-response for triage and containment
red-team	Red team exercises generate realistic TTPs that inform hunt hypothesis prioritization
cloud-security	Cloud posture findings (open S3, IAM wildcards) create hunting targets for data exfiltration TTPs
security-pen-testing	Pen test findings identify attack surfaces that threat hunting should monitor post-remediation