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SKILL.md
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):
Alternatively on Ubuntu via the GIFT PPA:
docker pull log2timeline/plaso
# Run a tool, mounting your evidence/output directory
docker run -v /cases:/data log2timeline/plaso log2timeline.py --version
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.
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:
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.
