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performing-timeline-reconstruction-with-plaso Builds comprehensive forensic super-timelines using Plaso (log2timeline and psort) to correlate events across file system metadata, event logs, browser history, and registry artifacts into a unified chronological view. Use during complex forensic investigations that need cross-source event correlation, or when standard log analysis is insufficient to establish the sequence of activities for reporting findings.
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下载 Zip 下载中... name performing-timeline-reconstruction-with-plaso description Builds comprehensive forensic super-timelines using Plaso (log2timeline and psort) to correlate events across file system metadata, event logs, browser history, and registry artifacts into a unified chronological view. Use during complex forensic investigations that need cross-source event correlation, or when standard log analysis is insufficient to establish the sequence of activities for reporting findings. domain cybersecurity subdomain digital-forensics tags ["forensics","timeline-analysis","plaso","log2timeline","super-timeline","event-correlation"] version 1.0 author mahipal license Apache-2.0 nist_csf ["RS.AN-03","DE.AE-02","RS.MA-01"] mitre_attack ["T1005","T1074","T1119","T1070","T1059"]
Performing Timeline Reconstruction with Plaso
When to Use
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
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
sudo add-apt-repository ppa:gift/stable
sudo apt-get update
sudo apt-get install plaso-tools
pip install plaso
docker pull log2timeline/plaso
log2timeline.py --version
psort.py --version
mkdir -p /cases/case-2024-001/timeline/
img_stat /cases/case-2024-001/images/evidence.dd
Step 2: Generate the Plaso Storage File with log2timeline
log2timeline.py \
--storage-file /cases/case-2024-001/timeline/evidence.plaso \
/cases/case-2024-001/images/evidence.dd
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
cat << '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
rm
Step 3: Filter and Export Timeline with psort
psort.py \
-o l2tcsv \
-w /cases/case-2024-001/timeline/full_timeline.csv \
/cases/case-2024-001/timeline/evidence.plaso
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'"
psort.py \
-o json_line \
-w /cases/case-2024-001/timeline/timeline.jsonl \
/cases/case-2024-001/timeline/evidence.plaso
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'"
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
git clone https://github.com/google/timesketch.git
cd timesketch
docker compose up -d
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
timesketch_importer \
--host http://localhost:5000 \
--username analyst \
--sketch_id 1 \
--timeline_name "Case 2024-001" \
/cases/case-2024-001/timeline/timeline.jsonl
Step 5: Perform Targeted Timeline Analysis
python3 << 'PYEOF'
import csv
from collections import defaultdict
from datetime import datetime
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
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 ===" )
for source , 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}" )
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
Tools & Systems Tool Purpose log2timeline (Plaso) Primary timeline generation engine parsing 100+ artifact types psort Plaso output filtering, sorting, and export utility Timesketch Web-based collaborative forensic timeline analysis platform Timeline Explorer 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.
Output Format Timeline Reconstruction Summary:
Evidence Sources:
Disk Image: evidence.dd (500 GB, NTFS)
Plaso Storage: evidence.plaso (2.3 GB)
Processing Statistics:
Total events extracted: 4,567,890
Parsers used: 45 (winevtx, prefetch, mft, usnjrnl, lnk, chrome, firefox, winreg, ...)
Processing time: 3h 45m
Incident Window (2024-01-15 to 2024-01-20):
Events in window: 234,567
Event Sources:
MFT: 89,234
Event Logs: 45,678
USN Journal: 56,789
Registry: 23,456
Prefetch: 1,234
Browser: 5,678
LNK Files: 2,345
Other: 10,153
Key Timeline Events:
2024-01-15 14:32 - Phishing email opened (browser)
2024-01-15 14:33 - Malicious document downloaded
2024-01-15 14:35 - PowerShell executed (Prefetch + Event Log)
2024-01-15 14:36 - C2 connection established (Registry + Event Log)
2024-01-16 02:30 - Mimikatz execution (Prefetch)
2024-01-16 02:45 - Lateral movement to DC (Event Log)
2024-01-17 03:00 - Data exfiltration (MFT + USN Journal)
2024-01-18 03:00 - Log clearing (Event Log)
Exported Files:
Full Timeline: /timeline/full_timeline.csv (4.5M rows)
Incident Window: /timeline/incident_window.csv (234K rows)
Timesketch Import: /timeline/timeline.jsonl
