/building-super-timelines-with-plaso

name: building-super-timelines-with-plaso
description: Generate forensic super-timelines with Plaso's log2timeline.py, pinfo.py,
  psort.py, and psteal.py CLI tools (fusing file-system MACB, registry, EVTX, browser
  history, prefetch, LNK, and more), then triage and filter the results in Timesketch.
  Use when reconstructing the full sequence of events on a compromised or forensically
  imaged host during a DFIR investigation.
domain: cybersecurity
subdomain: digital-forensics
tags:
- digital-forensics
- plaso
- log2timeline
- super-timeline
- timesketch
- dfir
- timeline-analysis
- incident-response
version: '1.0'
author: mahipal
license: Apache-2.0
nist_csf:
- RS.AN-03
mitre_attack:
- T1070

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 (timest