Project · Beginner · about 12 hours
Triage a day of logs and write detections
Analyse a set of security logs, separate noise from a real intrusion, and write and test Sigma detections for what you find.
Skills:Linux and Bash, Python, SIEM and security operations
Part of: Cybersecurity Engineer
Brief
A security operations centre lives on logs. Build a realistic set (extend the generator from the SIEM shelf, or use a public sample), find the intrusion hidden in the noise, and turn your findings into tested, reusable detections.
What to build
Produce a labelled log set that mixes normal activity, internet scanning and one multi-step intrusion (guessing, a success, privilege use, persistence)
Triage it: identify the incident, build a timeline, and write down what you dismissed as noise and why
Write Sigma rules for the key steps and one correlation (failures then success), each mapped to an ATT&CK technique
Build a test harness that runs the rules against known-bad and known-good logs and reports false positives and negatives
Write the analyst report: what happened, evidence, and recommended actions
Deliverables
Repository with
logs/,rules/,tests/andREPORT.md
How it is graded
Reviewers run your test harness and add a new benign pattern to check your rules do not fire on it.
Stretch
Load your rules and logs into a free SIEM (for example Elastic or Wazuh) and reproduce the alerts there.
Deliverables
- A log generator or captured sample and a written triage of what happened, with a timeline
- At least four Sigma rules mapped to MITRE ATT&CK techniques
- A test harness that runs each rule against known-bad and known-good logs and reports hits
- A short report an analyst could act on
How a reviewer grades it
| Criterion | Weight | What good looks like |
|---|---|---|
| Triage correctly separates scanner noise from the real intrusion | 30% | |
| Detections fire on the attack and not on normal activity, proven by tests | 35% | |
| Rules are mapped to ATT&CK and documented with triage steps | 20% | |
| Report is clear and actionable | 15% |