Building Super Timelines with Plaso

Authorized Use Only: Build timelines only from evidence you are authorized to analyze. Work from forensic images/copies and preserve chain of custody.

Overview

Plaso (Plaso Langar Að Safna Öllu) is the open-source engine behind log2timeline, the standard for building forensic super timelines — a single chronological, normalized view fusing hundreds of artifact types (file-system MACB times, registry, EVTX, browser history, prefetch, LNK, $UsnJrnl, syslog, and more) into one timeline. Plaso has three core CLI tools:

• log2timeline.py — extracts events from a source (disk image, mount point, directory, or device) into a .plaso storage file using its large parser/plugin set.
• pinfo.py — reports on the contents and processing metadata of a .plaso file.
• psort.py — post-processes, filters, deduplicates, time-zones, and exports the storage file to an output format (CSV, JSON-line, Elasticsearch, Timesketch, etc.).
• psteal.py — convenience wrapper that runs extraction + export in one step.

The resulting timeline is enormous, so analysts triage it in Timesketch — a collaborative, web-based timeline analysis platform that ingests .plaso files (or CSV/JSONL) and supports filtering, tagging, starring, saved searches, and automated analyzers.

When to Use

• Reconstructing the full sequence of events on a compromised host during incident response.
• Correlating activity across many artifact sources on a single normalized timeline.
• Investigating anti-forensic behavior such as timestomping or log clearing (which stands out against MACB and journal evidence).
• Feeding a curated timeline into Timesketch for team triage.

Prerequisites

• Install Plaso (Docker is the supported, reproducible method):

  docker pull log2timeline/plaso
  # Run a tool, mounting your evidence/output directory
  docker run -v /cases:/data log2timeline/plaso log2timeline.py --version
  

  Alternatively on Ubuntu via the GIFT PPA:

  sudo add-apt-repository ppa:gift/stable
  sudo apt-get update && sudo apt-get install -y plaso-tools
  

• A Timesketch instance (docker-compose deployment from https://github.com/google/timesketch) for triage.
• A forensic image (E01/raw) or mounted file system.

Objectives

• Extract events from an image into a .plaso storage file.
• Inspect the storage file with pinfo.
• Filter and export a focused super timeline with psort.
• Import the timeline into Timesketch and triage it.

MITRE ATT&CK Mapping

ID	Official Technique Name	Relevance to this skill
T1070	Indicator Removal	Super timelines reveal indicator-removal behavior (log clearing, file deletion, timestomping) by exposing inconsistencies between MACB timestamps, the USN journal, and event logs.

Plaso is a defensive forensics engine; the mapping reflects the anti-forensic adversary behavior super timelines are well suited to detect.

Workflow

1. Extract events into a storage file

log2timeline.py writes a .plaso file from a source. --storage-file names the output; the source can be an .E01, raw image, mount point, or directory.

log2timeline.py --storage-file timeline.plaso /cases/greendale/image.E01

Scope parsers for speed/relevance with --parsers (presets like win7, webhist, or explicit parser names):

log2timeline.py --parsers "win7,!filestat" --storage-file timeline.plaso /cases/image.E01

2. Inspect the storage file

pinfo.py reports source, parsers used, event counts, and any warnings.

pinfo.py timeline.plaso

3. Export a filtered super timeline (CSV)

psort.py selects an output module with -o, writes with -w, normalizes the timezone with --output-time-zone, and accepts an event filter expression to scope a date range.

psort.py --output-time-zone 'UTC' \
  -o l2tcsv \
  -w supertimeline.csv \
  timeline.plaso \
  "date > datetime('2026-01-01T00:00:00') AND date < datetime('2026-01-27T00:00:00')"

For Timesketch-friendly JSON lines, use the json_line output module:

psort.py --output-time-zone 'UTC' -o json_line -w supertimeline.jsonl timeline.plaso

4. One-step extraction + export with psteal

psteal.py runs extraction and CSV export together for quick triage.

psteal.py --source /cases/greendale/image.E01 -o l2tcsv -w supertimeline.csv

5. Import into Timesketch

Use the official timesketch_importer CLI to upload the .plaso (or CSV/JSONL) into a sketch. Timesketch chunks/reassembles and indexes the file.

timesketch_importer \
  --host http://127.0.0.1:5000 \
  --username admin \
  --timeline_name "greendale-host01" \
  --sketch_id 1 \
  timeline.plaso

6. Triage in Timesketch

In the sketch UI:

• Filter to a suspicious window or data_type (e.g. windows:evtx:record, fs:stat).
• Star/tag events of interest and add comments for collaboration.
• Save searches and run analyzers (e.g. browser timeframe, similarity, sigma) over the timeline.
• Build a narrative from corroborating events across artifact sources.

7. Hunt for anti-forensics

Look for MACB timestamps that disagree with $UsnJrnl entries (timestomping), gaps or EventLog cleared (1102) records, and deleted-then-recreated files — all visible on the unified timeline.

Tools and Resources

Resource	Purpose	Link
Plaso (log2timeline)	Timeline engine + tools	https://github.com/log2timeline/plaso
Plaso documentation	Tool usage and parsers	https://plaso.readthedocs.io/
Timesketch	Timeline analysis platform	https://github.com/google/timesketch
Timesketch docs	Deployment, importer, analyzers	https://timesketch.org/
Plaso Docker image	Reproducible runtime	https://hub.docker.com/r/log2timeline/plaso

Key Commands

Command	Purpose
log2timeline.py --storage-file out.plaso <source>	Extract events
log2timeline.py --parsers <preset> ...	Scope parsers
pinfo.py out.plaso	Inspect storage file
psort.py -o l2tcsv -w out.csv out.plaso "<filter>"	Filter + export CSV
psort.py -o json_line -w out.jsonl out.plaso	Export JSONL
psteal.py --source <img> -o l2tcsv -w out.csv	Extract + export in one step
timesketch_importer --host ... <file>	Import into Timesketch

Validation Criteria

• .plaso storage file produced from the source image
• pinfo confirms expected parsers ran and event counts are non-zero
• Super timeline exported with UTC normalization and a scoped filter
• Timeline imported into a Timesketch sketch and indexed
• Suspicious window triaged with tags/stars/saved searches
• Anti-forensic indicators (timestomping, log clearing) checked
• Findings documented with corroborating cross-source events