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
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