Performing Timeline Reconstruction With Plaso

# Install Plaso on Ubuntu/Debian
sudo 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 directory
mkdir -p /cases/case-2024-001/timeline/

# Verify the forensic image
img_stat /cases/case-2024-001/images/evidence.dd

# 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 paths
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
/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

# 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

# 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

# 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 ===")
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}")

# 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