Generating Forensic Timelines With Hayabusa

Generating Forensic Timelines with Hayabusa

Overview

Hayabusa (隼, Japanese for "peregrine falcon") is a Sigma-based threat-hunting and fast-forensics timeline generator for Windows event logs, developed by Yamato Security in Rust. It parses .evtx files (offline or via live analysis of a local host), applies a large built-in library of Sigma detection rules plus Hayabusa-specific rules, and produces a single, readable, chronological timeline of high-signal events with severity levels, MITRE ATT&CK tactics, and rule references. This collapses thousands of raw event-log records into a prioritized incident timeline that an analyst can review quickly.

Hayabusa is purpose-built for DFIR triage. Instead of loading EVTX into a SIEM, an investigator runs a single binary against a directory of collected logs and gets a CSV or JSON timeline plus metrics (events per computer, per Event ID, per channel). Because detections are Sigma-based, coverage tracks the open detection-engineering community, and rules can be updated on demand with update-rules. The tool's output integrates with downstream analysis: CSV opens in Timeline Explorer, JSONL feeds into jq, and timesketch-* profiles export directly into Timesketch.

A frequent finding in Hayabusa timelines is malicious PowerShell — MITRE ATT&CK T1059.001 (Command and Scripting Interpreter: PowerShell) — surfaced via Sigma rules over Event ID 4104 (script-block logging), 4103, and Sysmon process creation. This skill maps to NIST CSF RS.AN-03 (analysis is performed to establish what has taken place during an incident).

When to Use

• During incident-response triage, to turn a pile of collected .evtx files into a prioritized timeline.
• When you need fast, SIEM-free detection over Windows event logs with community Sigma coverage.
• To enumerate suspicious activity (PowerShell, account changes, lateral movement) across many hosts' logs.
• To produce metrics (events per computer/Event ID/channel) and pivot keywords for deeper hunting.
• To export an incident timeline into Timesketch or Timeline Explorer for collaborative analysis.

Prerequisites

• Hayabusa binary. Download a pre-compiled release (Windows/Linux/macOS) from GitHub:

  # Linux example
  curl -LO https://github.com/Yamato-Security/hayabusa/releases/latest/download/hayabusa-3.0.0-lin-x64-gnu.zip
  unzip hayabusa-*.zip && cd hayabusa-*
  ./hayabusa-3.0.0-lin-x64-gnu --version
  

  Or build from source (rules are a submodule):

  git clone https://github.com/Yamato-Security/hayabusa.git --recursive
  cd hayabusa && cargo build --release
  

• Collected Windows .evtx files (or run with --live-analysis on the host, as Administrator).
• Updated detection rules:

  ./hayabusa update-rules
  

• Optional: Timeline Explorer (Windows) or Timesketch for visualizing output; jq for JSONL.

Objectives

• Build a CSV or JSON forensic timeline from a directory of .evtx files.
• Update and tune the Sigma rule set used for detection.
• Select an output profile appropriate to the investigation (minimal vs. verbose vs. timesketch).
• Generate metrics (computer, Event ID, log) and pivot keywords for hunting leads.
• Filter the timeline by minimum severity to focus triage.
• Search logs for specific IOCs by keyword or regex.

MITRE ATT&CK Mapping

Workflow

1. Update the rule set

Pull the latest Sigma and Hayabusa rules before every investigation.

./hayabusa update-rules

2. Build a CSV timeline from collected logs

Point Hayabusa at a directory of .evtx files and write a CSV timeline. -w skips the interactive wizard for scripted runs.

./hayabusa csv-timeline -d ./collected_evtx -o timeline.csv -w
# UTC timestamps for cross-host correlation
./hayabusa csv-timeline -d ./collected_evtx -o timeline_utc.csv -U -w

3. Choose an output profile

Profiles control detail. Use verbose to include MITRE ATT&CK tactics, tags, and the source rule/EVTX file; all-field-info to retain every original field.

# Verbose: adds MITRE tactics, tags, rule file, evtx file
./hayabusa csv-timeline -d ./collected_evtx -o timeline_verbose.csv -p verbose -w
# Preserve all original event fields
./hayabusa csv-timeline -d ./collected_evtx -o timeline_full.csv -p all-field-info -w

Available profiles: minimal, standard (default), verbose, all-field-info, all-field-info-verbose, super-verbose, timesketch-minimal, timesketch-verbose.

4. Filter by minimum severity

Focus triage on the highest-confidence detections with -m/--min-level.

./hayabusa csv-timeline -d ./collected_evtx -o critical.csv -m high -w

Levels: informational, low, medium, high, critical.

5. Produce a JSON/JSONL timeline for tooling

JSONL feeds cleanly into jq and downstream pipelines.

./hayabusa json-timeline -d ./collected_evtx -L -o timeline.jsonl -w
# Example: top rule titles
jq -r '.RuleTitle' timeline.jsonl | sort | uniq -c | sort -rn | head

6. Generate metrics and pivot keywords

Summaries reveal which hosts and Event IDs dominate, and pivot keywords give hunting leads.

./hayabusa computer-metrics -d ./collected_evtx -o computers.csv
./hayabusa eid-metrics -d ./collected_evtx -o eid.csv
./hayabusa log-metrics -d ./collected_evtx -o logs.csv
./hayabusa pivot-keywords-list -d ./collected_evtx -m medium -o pivots

7. Search logs for specific IOCs

Use the search command for keyword or regex hunting independent of detection rules.

# Keyword search (case-insensitive) for a suspicious binary
./hayabusa search -d ./collected_evtx -k "powershell" -i
# Regex search for base64-looking PowerShell encoded commands
./hayabusa search -d ./collected_evtx -r "-[Ee]nc(odedCommand)?\s+[A-Za-z0-9+/=]{20,}"

8. Live triage on a running host

On the affected machine (Administrator), analyze local logs without exporting first.

./hayabusa csv-timeline -l -o live_timeline.csv -m high -w

Tools and Resources

Validation Criteria

• Hayabusa binary installed and --version confirmed.
• Rules updated with update-rules before analysis.
• CSV timeline generated from the collected .evtx directory.
• Appropriate output profile selected for the investigation goal.
• Timeline filtered by minimum severity for triage focus.
• JSON/JSONL timeline produced for downstream tooling where needed.
• Computer/EID/log metrics generated.
• Pivot keywords list produced for hunting leads.
• Targeted IOC searches run with keyword/regex.
• Findings (e.g., PowerShell T1059.001 detections) documented in the incident timeline.