Build comprehensive forensic super-timelines using Plaso (log2timeline) to correlate events across file systems, logs, and artifacts into a unified chronological view.
Build comprehensive forensic super-timelines using Plaso (log2timeline) to correlate events across file systems, logs, and artifacts into a unified chronological view.
When building a comprehensive forensic timeline from multiple evidence sources
For correlating events across file system metadata, event logs, browser history, and registry
During complex investigations requiring chronological reconstruction of activities
When standard log analysis is insufficient to establish the sequence of events
For presenting investigation findings in a visual, chronological format
Detection Gaps & Validation
A super-timeline's greatest weakness is that it makes noise and forged timestamps look identical to ground truth. Guard against these:
Timezone and clock errors corrupt the whole timeline. Plaso stores events in UTC, but parsers infer source timezones; run log2timeline.py with the correct --timezone and confirm the image's TimeZoneInformation registry value. A wrong offset or a host whose clock was manually changed silently shifts events, fabricating false sequences. State the timezone basis in every report.
Super-timeline noise buries the signal. A full-image run emits millions of events; $MFT/$UsnJrnl alone can dominate. Without a --filter-file or psort date-range/source filter you will drown. Pivot from known anchors (malware execution, login) and expand outward rather than reading top-to-bottom.
MACB timestamps are stompable and parser-dependent. Anti-forensic tooling backdates $STANDARD_INFORMATION times that Plaso surfaces; the harder-to-forge $FILE_NAME ($FN) times come from a separate MFT parse. Compare $SI vs $FN to spot timestomping, and corroborate a file's "creation" against USN Journal, Prefetch/Amcache execution, and event logs before trusting it.
Coverage gaps = missing parsers/artifacts, not absence of activity. If you ran a targeted --parsers list, anything outside it is invisible; deleted/rotated logs and VSS snapshots won't appear unless included. "No evidence in the timeline" must be qualified by which parsers and sources were actually processed.
Interpretation false positives. A single artifact's timestamp ≠ user action: $MFT access times are unreliable on modern Windows, antivirus/indexing/backup touch files en masse, and Prefetch reflects execution by any caller. Confirm a pivotal event with at least two independent artifact types (e.g., Prefetch + 4688 + LNK) before placing it in the narrative.
Prerequisites
Plaso (log2timeline/psort) installed on forensic workstation
Forensic disk image(s) in raw (dd), E01, or VMDK format
Sufficient storage for Plaso output (can be 10x+ the image size)
Minimum 8GB RAM (16GB+ recommended for large images)
Timeline Explorer (Eric Zimmerman) or Timesketch for visualization
Understanding of timestamp types (MACB: Modified, Accessed, Changed, Born)
Workflow
Step 1: Install Plaso and Prepare the Environment
# Install Plaso on Ubuntu/Debiansudo add-apt-repository ppa:gift/stable
sudo apt-get update
sudo apt-get install plaso-tools
# Or install via pip
pip install plaso
# Or use Docker (recommended for dependency isolation)
docker pull log2timeline/plaso
# Verify installation
log2timeline.py --version
psort.py --version
# Create output directorymkdir -p /cases/case-2024-001/timeline/
# Verify the forensic image
img_stat /cases/case-2024-001/images/evidence.dd
Step 2: Generate the Plaso Storage File with log2timeline
# Basic processing of a disk image (all parsers)
log2timeline.py \
--storage-file /cases/case-2024-001/timeline/evidence.plaso \
/cases/case-2024-001/images/evidence.dd
# Process with specific parsers for faster targeted analysis
log2timeline.py \
--parsers "winevtx,prefetch,mft,usnjrnl,lnk,recycle_bin,chrome_history,firefox_history,winreg" \
--storage-file /cases/case-2024-001/timeline/evidence.plaso \
/cases/case-2024-001/images/evidence.dd
# Process with a filter file to focus on specific pathscat << 'EOF' > /cases/case-2024-001/timeline/filter.txt
/Windows/System32/winevt/Logs
/Windows/Prefetch
/Users/*/NTUSER.DAT
/Users/*/AppData/Local/Google/Chrome
/Users/*/AppData/Roaming/Mozilla/Firefox
/$MFT
/$UsnJrnl:$J
/Windows/System32/config
EOF
log2timeline.py \
--filter-file /cases/case-2024-001/timeline/filter.txt \
--storage-file /cases/case-2024-001/timeline/evidence.plaso \
/cases/case-2024-001/images/evidence.dd
# Using Docker
docker run --rm -v /cases:/cases log2timeline/plaso log2timeline \
--storage-file /cases/case-2024-001/timeline/evidence.plaso \
/cases/case-2024-001/images/evidence.dd
# Process multiple evidence sources into one timeline
log2timeline.py \
--storage-file /cases/case-2024-001/timeline/combined.plaso \
/cases/case-2024-001/images/workstation.dd
log2timeline.py \
--storage-file /cases/case-2024-001/timeline/combined.plaso \
/cases/case-2024-001/images/server.dd
Step 3: Filter and Export Timeline with psort
# Export full timeline to CSV (super-timeline format)
psort.py \
-o l2tcsv \
-w /cases/case-2024-001/timeline/full_timeline.csv \
/cases/case-2024-001/timeline/evidence.plaso
# Export with date range filter (focus on incident window)
psort.py \
-o l2tcsv \
-w /cases/case-2024-001/timeline/incident_window.csv \
/cases/case-2024-001/timeline/evidence.plaso \
"date > '2024-01-15 00:00:00' AND date < '2024-01-20 23:59:59'"# Export in JSON Lines format (for ingestion into SIEM/Timesketch)
psort.py \
-o json_line \
-w /cases/case-2024-001/timeline/timeline.jsonl \
/cases/case-2024-001/timeline/evidence.plaso
# Export with specific source type filters
psort.py \
-o l2tcsv \
-w /cases/case-2024-001/timeline/registry_events.csv \
/cases/case-2024-001/timeline/evidence.plaso \
"source_short == 'REG'"
psort.py \
-o l2tcsv \
-w /cases/case-2024-001/timeline/evtx_events.csv \
/cases/case-2024-001/timeline/evidence.plaso \
"source_short == 'EVT'"# Export for Timeline Explorer (dynamic CSV)
psort.py \
-o dynamic \
-w /cases/case-2024-001/timeline/timeline_explorer.csv \
/cases/case-2024-001/timeline/evidence.plaso
Step 4: Analyze Timeline with Timesketch
# Install Timesketch (Docker deployment)
git clone https://github.com/google/timesketch.git
cd timesketch
docker compose up -d
# Import Plaso file into Timesketch via CLI
timesketch_importer \
--host http://localhost:5000 \
--username analyst \
--password password \
--sketch_id 1 \
--timeline_name "Case 2024-001 Workstation" \
/cases/case-2024-001/timeline/evidence.plaso
# Alternatively, import JSONL
timesketch_importer \
--host http://localhost:5000 \
--username analyst \
--sketch_id 1 \
--timeline_name "Case 2024-001" \
/cases/case-2024-001/timeline/timeline.jsonl
# In Timesketch web UI:# 1. Search for events: "data_type:windows:evtx:record AND event_identifier:4624"# 2. Apply Sigma analyzers for automated detection# 3. Star/tag important events# 4. Create stories documenting the investigation narrative# 5. Share with team members
Step 5: Perform Targeted Timeline Analysis
# Analyze specific time periods around known events
python3 << 'PYEOF'
import csv
from collections import defaultdict
from datetime import datetime
# Load incident window timeline
events_by_hour = defaultdict(list)
source_counts = defaultdict(int)
with open('/cases/case-2024-001/timeline/incident_window.csv', 'r', errors='ignore') as f:
reader = csv.DictReader(f)
total = 0
for row in reader:
total += 1
timestamp = row.get('datetime', row.get('date', ''))
source = row.get('source_short', row.get('source', 'Unknown'))
description = row.get('message', row.get('desc', ''))
source_counts[source] += 1
# Group by hour for activity patterns
try:
dt = datetime.strptime(timestamp[:19], '%Y-%m-%dT%H:%M:%S')
hour_key = dt.strftime('%Y-%m-%d %H:00')
events_by_hour[hour_key].append({
'time': timestamp,
'source': source,
'description': description[:200]
})
except (ValueError, TypeError):
pass
print(f"Total events in incident window: {total}\n")
print("=== EVENTS BY SOURCE TYPE ===")
forsource, count in sorted(source_counts.items(), key=lambda x: x[1], reverse=True):
print(f" {source}: {count}")
print("\n=== ACTIVITY BY HOUR ===")
for hour in sorted(events_by_hour.keys()):
count = len(events_by_hour[hour])
bar = '#' * min(count // 10, 50)
print(f" {hour}: {count:>6} events {bar}")
# Find hours with unusual activity spikes
avg = total / max(len(events_by_hour), 1)
print(f"\n=== ANOMALOUS HOURS (>{avg*3:.0f} events) ===")
for hour in sorted(events_by_hour.keys()):
if len(events_by_hour[hour]) > avg * 3:
print(f" {hour}: {len(events_by_hour[hour])} events (SPIKE)")
PYEOF
Key Concepts
Concept
Description
Super-timeline
Unified chronological view combining all artifact timestamps from multiple sources
MACB timestamps
Modified, Accessed, Changed (metadata), Born (created) - four key file timestamp types
Plaso storage file
SQLite-based intermediate format storing parsed events before export
L2T CSV
Log2timeline CSV format with standardized columns for timeline events
Parser
Plaso module extracting timestamps from a specific artifact type (e.g., winevtx, prefetch)
Psort
Plaso sorting and filtering tool for post-processing storage files
Timesketch
Google open-source collaborative timeline analysis platform
Pivot points
Known timestamps (e.g., malware execution) used to focus investigation scope
Eric Zimmerman's Windows GUI for CSV timeline analysis
KAPE
Automated triage collection feeding into Plaso processing
mactime (TSK)
Simpler timeline generation from Sleuth Kit bodyfiles
Excel/Sheets
Manual timeline review for small filtered datasets
Elastic/Kibana
Alternative visualization platform for JSONL timeline data
Common Scenarios
Scenario 1: Ransomware Attack Reconstruction
Process the full disk image with Plaso, filter to the week before encryption was discovered, identify the initial access vector from browser history and event logs, trace privilege escalation through registry and Prefetch, map lateral movement from network logon events, pinpoint encryption start from MFT timestamps showing mass file modifications.
Scenario 2: Data Theft Investigation
Create super-timeline from suspect's workstation, filter for USB device connection events, file access timestamps, and cloud storage browser activity, build a narrative showing data staging, compression, and exfiltration, present timeline to legal team with tagged evidence points.
Scenario 3: Multi-System Breach Analysis
Process disk images from all affected systems into a single Plaso storage file, import into Timesketch for collaborative analysis, search for lateral movement patterns across system timelines, identify the patient-zero system and initial compromise vector, map the full attack chain across the environment.
Scenario 4: Insider Threat After-Hours Activity
Filter timeline to non-business hours only, identify file access patterns outside normal working times, correlate with authentication events (badge access, VPN logon), search for data access to sensitive directories during these periods, build evidence package for HR/legal.
