SOC › Module 2 › Lesson 1
Hunt Methodology
Hunt methodology literacy names structured proactive search — scope, data, hypothesis, investigate, document — mapped on YOUR $HUNT_LAB before any live hunt campaign.
Visual · t30_hunt_methodology
Hunt methodology literacy. $HUNT_LAB only. Original Cyberlium.
Opening
Reactive alerts are not enough — literacy names how L2 hunts proactively on authorized telemetry, not on stranger production clusters.
Threat hunting methodology follows structured steps: define scope and data sources, form a testable hypothesis, search telemetry for evidence, validate or refute, document findings, and feed detection improvements. Mature programs align hunts to risk, ATT&CK coverage gaps, and intel — always within policy. Cyberlium maps hunt methodology on YOUR $HUNT_LAB synthetic telemetry — never hunt stranger employer data without authorization. Next: Hypothesis-Based.
1. Hunt methodology steps (named)
Scope: which assets and time range on lab data. Data: auth jsonl, process logs, proxy — sources you own. Hypothesis: testable statement ('unusual failed auth cluster on LAB-WS-01'). Investigate: search, correlate, validate. Document: findings card for L3 or detection team.
On $HUNT_LAB, write one sentence per step for a fake brute-force hunt scenario.
Command guide
Try these commands — Hunt methodology steps (named)
═══ TOOLS & WEBSITES ═══ Browse / read these (authorized learning only — stay in YOUR lab / program scope)
Threat hunt methodology — https://attack.mitre.org/ (hypothesis-driven hunting) NIST IR — https://csrc.nist.gov/publications/detail/sp/800-61/rev-2/final SANS hunting — https://www.sans.org/white-papers/threat-hunting/ (literacy context)
═══ INSTALL ═══
Linux (Debian/Ubuntu):
Command — copy this
sudo apt install python3 sudo apt install curl
macOS:
Command — copy this
brew install python3
Windows: Download https://python.org/downloads/ Built-in (PowerShell: Invoke-WebRequest)
═══ LINUX / macOS ═══
Command — copy this
export HUNT_LAB=${HUNT_LAB:-$HOME/cyberlium-lab/t30-hunt}
python3 - <<'PY'
steps = ['Define hypothesis from intel/ATT&CK', 'Collect: query YOUR seeded telemetry', 'Analyze: rare parent, logon sequence', 'Uncover: document findings', 'Enrich: MITRE map + detection gap']
print('Threat hunt methodology (YOUR lab):')
for i, s in enumerate(steps, 1): print(f' {i}. {s}')
PYCommand — copy this
curl -sS https://attack.mitre.org/ | head -8
Primary tools to practice this lesson: python3, curl. Reference sites: Threat hunt methodology (https://attack.mitre.org/); NIST IR (https://csrc.nist.gov/publications/detail/sp/800-61/rev-2/final); SANS hunting (https://www.sans.org/white-papers/threat-hunting/). Run every command in the box — install first, then the usage lines — only on YOUR lab / program scope.
2. Why methodology beats random searching
Unstructured SIEM clicking wastes time and misses root cause. Methodology gives reproducible hunts, measurable coverage, and audit trail for leadership. Employers expect documented hunt outcomes — not mystery queries.
Students learn the steps on fictional hosts before real PII appears in live consoles.
3. Lab boundary
Forbidden: claiming hunt skill lets you query any security dashboard. Allowed: methodology card — five steps tied to $HUNT_LAB sample scenario.
Ship: hunt methodology card for YOUR lab scenario. Next: Hypothesis-Based.
4. What you ship: hunt methodology card for $HUNT_LAB
Five methodology steps on sample scenario. $HUNT_LAB named. NO live tenant work. chmod 600.
5. What you record before the next lesson
Date. Methodology card. $HUNT_LAB named. File t30-m02-l01-hunt-methodology.txt chmod 600.
6. Wrong vs right: stranger prod vs YOUR hunt telemetry
Worked failure — same MSF word, opposite target. Right never needs a café Wi-Fi or classmate laptop.
Wrong
Run unbounded production searches without hypothesis. Treat lab as license for stranger SIEM.
Right
Write hunt methodology card for YOUR $HUNT_LAB scenario. Next: Hypothesis-Based.
Mission: map hunt methodology on YOUR lab scenario
1) Pick one $HUNT_LAB synthetic scenario. 2) Write scope/data/hypothesis/investigate/document steps. 3) Note escalation trigger if confirmed. 4) chmod 600.
Stuck? Ask Cyberlium AI Mentor
Hypothesis before query — methodology saves clusters and careers.
Knowledge Check
APPLY: Hunt methodology includes:
Multiple choice
Knowledge Check
APPLY: True or False: Cyberlium hunt practice uses $HUNT_LAB sample telemetry only.
True or False
Knowledge Check
APPLY: Hunt methodology literacy on Cyberlium supports:
Multiple choice