[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6GZKEM4SWB20TKVEPSBA3QG":3,"skill-same:01M6GZKEM4SWB20TKVEPSBA3QG":86},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":17,"publishedAt":38,"linkOnly":39,"markdownContent":40,"markdownBody":41,"frontMatter":42,"refName":48,"commitSha":49,"skillDirectory":50,"forksCount":51,"repositoryHtmlUrl":52,"repositoryHomepageUrl":6,"ownerHtmlUrl":53,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":54,"install":55,"resources":68},"01M6GZKEM4SWB20TKVEPSBA3QG","performing-timeline-reconstruction-with-plaso",null,"Build comprehensive forensic super-timelines using Plaso (log2timeline) to correlate events across file systems, logs, and artifacts into a unified chronological view.","micsapp\u002Fmicstec-skills","micsapp","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F82411001?v=4","01M6GWCFQ6D3PV8EHNY62NN5VW","01M6GWCFVE1PA5HG0DKC7AMFJ3",4,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fmicsapp\u002Fmicstec-skills\u002Fblob\u002Fmaster\u002F_universal\u002Fcybersecurity-skills\u002Fskills\u002Fperforming-timeline-reconstruction-with-plaso\u002FSKILL.md",[],[18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37],"timeline","performing","with","performing timeline reconstruction with plaso","plaso","timeline-analysis","log2timeline","super-timeline","correlation","event-correlation","cybersecurity","analysis","logs","view","build","across","systems","event","file","artifacts","2026-11-10 10:12:30",false,"---\nname: performing-timeline-reconstruction-with-plaso\ndescription: Build comprehensive forensic super-timelines using Plaso (log2timeline) to correlate events across file systems, logs, and artifacts into a unified chronological view.\ndomain: cybersecurity\nsubdomain: digital-forensics\ntags: [forensics, timeline-analysis, plaso, log2timeline, super-timeline, event-correlation]\nversion: \"1.0\"\nauthor: mahipal\nlicense: Apache-2.0\n---\n\n# Performing Timeline Reconstruction with Plaso\n\n## When to Use\n- When building a comprehensive forensic timeline from multiple evidence sources\n- For correlating events across file system metadata, event logs, browser history, and registry\n- During complex investigations requiring chronological reconstruction of activities\n- When standard log analysis is insufficient to establish the sequence of events\n- For presenting investigation findings in a visual, chronological format\n\n## Prerequisites\n- Plaso (log2timeline\u002Fpsort) installed on forensic workstation\n- Forensic disk image(s) in raw (dd), E01, or VMDK format\n- Sufficient storage for Plaso output (can be 10x+ the image size)\n- Minimum 8GB RAM (16GB+ recommended for large images)\n- Timeline Explorer (Eric Zimmerman) or Timesketch for visualization\n- Understanding of timestamp types (MACB: Modified, Accessed, Changed, Born)\n\n## Workflow\n\n### Step 1: Install Plaso and Prepare the Environment\n\n```bash\n# Install Plaso on Ubuntu\u002FDebian\nsudo add-apt-repository ppa:gift\u002Fstable\nsudo apt-get update\nsudo apt-get install plaso-tools\n\n# Or install via pip\npip install plaso\n\n# Or use Docker (recommended for dependency isolation)\ndocker pull log2timeline\u002Fplaso\n\n# Verify installation\nlog2timeline.py --version\npsort.py --version\n\n# Create output directory\nmkdir -p \u002Fcases\u002Fcase-2024-001\u002Ftimeline\u002F\n\n# Verify the forensic image\nimg_stat \u002Fcases\u002Fcase-2024-001\u002Fimages\u002Fevidence.dd\n```\n\n### Step 2: Generate the Plaso Storage File with log2timeline\n\n```bash\n# Basic processing of a disk image (all parsers)\nlog2timeline.py \\\n   --storage-file \u002Fcases\u002Fcase-2024-001\u002Ftimeline\u002Fevidence.plaso \\\n   \u002Fcases\u002Fcase-2024-001\u002Fimages\u002Fevidence.dd\n\n# Process with specific parsers for faster targeted analysis\nlog2timeline.py \\\n   --parsers \"winevtx,prefetch,mft,usnjrnl,lnk,recycle_bin,chrome_history,firefox_history,winreg\" \\\n   --storage-file \u002Fcases\u002Fcase-2024-001\u002Ftimeline\u002Fevidence.plaso \\\n   \u002Fcases\u002Fcase-2024-001\u002Fimages\u002Fevidence.dd\n\n# Process with a filter file to focus on specific paths\ncat \u003C\u003C 'EOF' > \u002Fcases\u002Fcase-2024-001\u002Ftimeline\u002Ffilter.txt\n\u002FWindows\u002FSystem32\u002Fwinevt\u002FLogs\n\u002FWindows\u002FPrefetch\n\u002FUsers\u002F*\u002FNTUSER.DAT\n\u002FUsers\u002F*\u002FAppData\u002FLocal\u002FGoogle\u002FChrome\n\u002FUsers\u002F*\u002FAppData\u002FRoaming\u002FMozilla\u002FFirefox\n\u002F$MFT\n\u002F$UsnJrnl:$J\n\u002FWindows\u002FSystem32\u002Fconfig\nEOF\n\nlog2timeline.py \\\n   --filter-file \u002Fcases\u002Fcase-2024-001\u002Ftimeline\u002Ffilter.txt \\\n   --storage-file \u002Fcases\u002Fcase-2024-001\u002Ftimeline\u002Fevidence.plaso \\\n   \u002Fcases\u002Fcase-2024-001\u002Fimages\u002Fevidence.dd\n\n# Using Docker\ndocker run --rm -v \u002Fcases:\u002Fcases log2timeline\u002Fplaso log2timeline \\\n   --storage-file \u002Fcases\u002Fcase-2024-001\u002Ftimeline\u002Fevidence.plaso \\\n   \u002Fcases\u002Fcase-2024-001\u002Fimages\u002Fevidence.dd\n\n# Process multiple evidence sources into one timeline\nlog2timeline.py \\\n   --storage-file \u002Fcases\u002Fcase-2024-001\u002Ftimeline\u002Fcombined.plaso \\\n   \u002Fcases\u002Fcase-2024-001\u002Fimages\u002Fworkstation.dd\n\nlog2timeline.py \\\n   --storage-file \u002Fcases\u002Fcase-2024-001\u002Ftimeline\u002Fcombined.plaso \\\n   \u002Fcases\u002Fcase-2024-001\u002Fimages\u002Fserver.dd\n```\n\n### Step 3: Filter and Export Timeline with psort\n\n```bash\n# Export full timeline to CSV (super-timeline format)\npsort.py \\\n   -o l2tcsv \\\n   -w \u002Fcases\u002Fcase-2024-001\u002Ftimeline\u002Ffull_timeline.csv \\\n   \u002Fcases\u002Fcase-2024-001\u002Ftimeline\u002Fevidence.plaso\n\n# Export with date range filter (focus on incident window)\npsort.py \\\n   -o l2tcsv \\\n   -w \u002Fcases\u002Fcase-2024-001\u002Ftimeline\u002Fincident_window.csv \\\n   \u002Fcases\u002Fcase-2024-001\u002Ftimeline\u002Fevidence.plaso \\\n   \"date > '2024-01-15 00:00:00' AND date \u003C '2024-01-20 23:59:59'\"\n\n# Export in JSON Lines format (for ingestion into SIEM\u002FTimesketch)\npsort.py \\\n   -o json_line \\\n   -w \u002Fcases\u002Fcase-2024-001\u002Ftimeline\u002Ftimeline.jsonl \\\n   \u002Fcases\u002Fcase-2024-001\u002Ftimeline\u002Fevidence.plaso\n\n# Export with specific source type filters\npsort.py \\\n   -o l2tcsv \\\n   -w \u002Fcases\u002Fcase-2024-001\u002Ftimeline\u002Fregistry_events.csv \\\n   \u002Fcases\u002Fcase-2024-001\u002Ftimeline\u002Fevidence.plaso \\\n   \"source_short == 'REG'\"\n\npsort.py \\\n   -o l2tcsv \\\n   -w \u002Fcases\u002Fcase-2024-001\u002Ftimeline\u002Fevtx_events.csv \\\n   \u002Fcases\u002Fcase-2024-001\u002Ftimeline\u002Fevidence.plaso \\\n   \"source_short == 'EVT'\"\n\n# Export for Timeline Explorer (dynamic CSV)\npsort.py \\\n   -o dynamic \\\n   -w \u002Fcases\u002Fcase-2024-001\u002Ftimeline\u002Ftimeline_explorer.csv \\\n   \u002Fcases\u002Fcase-2024-001\u002Ftimeline\u002Fevidence.plaso\n```\n\n### Step 4: Analyze Timeline with Timesketch\n\n```bash\n# Install Timesketch (Docker deployment)\ngit clone https:\u002F\u002Fgithub.com\u002Fgoogle\u002Ftimesketch.git\ncd timesketch\ndocker compose up -d\n\n# Import Plaso file into Timesketch via CLI\ntimesketch_importer \\\n   --host http:\u002F\u002Flocalhost:5000 \\\n   --username analyst \\\n   --password password \\\n   --sketch_id 1 \\\n   --timeline_name \"Case 2024-001 Workstation\" \\\n   \u002Fcases\u002Fcase-2024-001\u002Ftimeline\u002Fevidence.plaso\n\n# Alternatively, import JSONL\ntimesketch_importer \\\n   --host http:\u002F\u002Flocalhost:5000 \\\n   --username analyst \\\n   --sketch_id 1 \\\n   --timeline_name \"Case 2024-001\" \\\n   \u002Fcases\u002Fcase-2024-001\u002Ftimeline\u002Ftimeline.jsonl\n\n# In Timesketch web UI:\n# 1. Search for events: \"data_type:windows:evtx:record AND event_identifier:4624\"\n# 2. Apply Sigma analyzers for automated detection\n# 3. Star\u002Ftag important events\n# 4. Create stories documenting the investigation narrative\n# 5. Share with team members\n```\n\n### Step 5: Perform Targeted Timeline Analysis\n\n```bash\n# Analyze specific time periods around known events\npython3 \u003C\u003C 'PYEOF'\nimport csv\nfrom collections import defaultdict\nfrom datetime import datetime\n\n# Load incident window timeline\nevents_by_hour = defaultdict(list)\nsource_counts = defaultdict(int)\n\nwith open('\u002Fcases\u002Fcase-2024-001\u002Ftimeline\u002Fincident_window.csv', 'r', errors='ignore') as f:\n    reader = csv.DictReader(f)\n    total = 0\n    for row in reader:\n        total += 1\n        timestamp = row.get('datetime', row.get('date', ''))\n        source = row.get('source_short', row.get('source', 'Unknown'))\n        description = row.get('message', row.get('desc', ''))\n\n        source_counts[source] += 1\n\n        # Group by hour for activity patterns\n        try:\n            dt = datetime.strptime(timestamp[:19], '%Y-%m-%dT%H:%M:%S')\n            hour_key = dt.strftime('%Y-%m-%d %H:00')\n            events_by_hour[hour_key].append({\n                'time': timestamp,\n                'source': source,\n                'description': description[:200]\n            })\n        except (ValueError, TypeError):\n            pass\n\nprint(f\"Total events in incident window: {total}\\n\")\n\nprint(\"=== EVENTS BY SOURCE TYPE ===\")\nfor source, count in sorted(source_counts.items(), key=lambda x: x[1], reverse=True):\n    print(f\"  {source}: {count}\")\n\nprint(\"\\n=== ACTIVITY BY HOUR ===\")\nfor hour in sorted(events_by_hour.keys()):\n    count = len(events_by_hour[hour])\n    bar = '#' * min(count \u002F\u002F 10, 50)\n    print(f\"  {hour}: {count:>6} events {bar}\")\n\n# Find hours with unusual activity spikes\navg = total \u002F max(len(events_by_hour), 1)\nprint(f\"\\n=== ANOMALOUS HOURS (>{avg*3:.0f} events) ===\")\nfor hour in sorted(events_by_hour.keys()):\n    if len(events_by_hour[hour]) > avg * 3:\n        print(f\"  {hour}: {len(events_by_hour[hour])} events (SPIKE)\")\nPYEOF\n```\n\n## Key Concepts\n\n| Concept | Description |\n|---------|-------------|\n| Super-timeline | Unified chronological view combining all artifact timestamps from multiple sources |\n| MACB timestamps | Modified, Accessed, Changed (metadata), Born (created) - four key file timestamp types |\n| Plaso storage file | SQLite-based intermediate format storing parsed events before export |\n| L2T CSV | Log2timeline CSV format with standardized columns for timeline events |\n| Parser | Plaso module extracting timestamps from a specific artifact type (e.g., winevtx, prefetch) |\n| Psort | Plaso sorting and filtering tool for post-processing storage files |\n| Timesketch | Google open-source collaborative timeline analysis platform |\n| Pivot points | Known timestamps (e.g., malware execution) used to focus investigation scope |\n\n## Tools & Systems\n\n| Tool | Purpose |\n|------|---------|\n| log2timeline (Plaso) | Primary timeline generation engine parsing 100+ artifact types |\n| psort | Plaso output filtering, sorting, and export utility |\n| Timesketch | Web-based collaborative forensic timeline analysis platform |\n| Timeline Explorer | Eric Zimmerman's Windows GUI for CSV timeline analysis |\n| KAPE | Automated triage collection feeding into Plaso processing |\n| mactime (TSK) | Simpler timeline generation from Sleuth Kit bodyfiles |\n| Excel\u002FSheets | Manual timeline review for small filtered datasets |\n| Elastic\u002FKibana | Alternative visualization platform for JSONL timeline data |\n\n## Common Scenarios\n\n**Scenario 1: Ransomware Attack Reconstruction**\nProcess 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.\n\n**Scenario 2: Data Theft Investigation**\nCreate 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.\n\n**Scenario 3: Multi-System Breach Analysis**\nProcess 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.\n\n**Scenario 4: Insider Threat After-Hours Activity**\nFilter 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\u002Flegal.\n\n## Output Format\n\n```\nTimeline Reconstruction Summary:\n  Evidence Sources:\n    Disk Image: evidence.dd (500 GB, NTFS)\n    Plaso Storage: evidence.plaso (2.3 GB)\n\n  Processing Statistics:\n    Total events extracted: 4,567,890\n    Parsers used: 45 (winevtx, prefetch, mft, usnjrnl, lnk, chrome, firefox, winreg, ...)\n    Processing time: 3h 45m\n\n  Incident Window (2024-01-15 to 2024-01-20):\n    Events in window: 234,567\n    Event Sources:\n      MFT:          89,234\n      Event Logs:   45,678\n      USN Journal:  56,789\n      Registry:     23,456\n      Prefetch:     1,234\n      Browser:      5,678\n      LNK Files:    2,345\n      Other:        10,153\n\n  Key Timeline Events:\n    2024-01-15 14:32 - Phishing email opened (browser)\n    2024-01-15 14:33 - Malicious document downloaded\n    2024-01-15 14:35 - PowerShell executed (Prefetch + Event Log)\n    2024-01-15 14:36 - C2 connection established (Registry + Event Log)\n    2024-01-16 02:30 - Mimikatz execution (Prefetch)\n    2024-01-16 02:45 - Lateral movement to DC (Event Log)\n    2024-01-17 03:00 - Data exfiltration (MFT + USN Journal)\n    2024-01-18 03:00 - Log clearing (Event Log)\n\n  Exported Files:\n    Full Timeline:     \u002Ftimeline\u002Ffull_timeline.csv (4.5M rows)\n    Incident Window:   \u002Ftimeline\u002Fincident_window.csv (234K rows)\n    Timesketch Import: \u002Ftimeline\u002Ftimeline.jsonl\n```\n","# Performing Timeline Reconstruction with Plaso\n\n## When to Use\n- When building a comprehensive forensic timeline from multiple evidence sources\n- For correlating events across file system metadata, event logs, browser history, and registry\n- During complex investigations requiring chronological reconstruction of activities\n- When standard log analysis is insufficient to establish the sequence of events\n- For presenting investigation findings in a visual, chronological format\n\n## Prerequisites\n- Plaso (log2timeline\u002Fpsort) installed on forensic workstation\n- Forensic disk image(s) in raw (dd), E01, or VMDK format\n- Sufficient storage for Plaso output (can be 10x+ the image size)\n- Minimum 8GB RAM (16GB+ recommended for large images)\n- Timeline Explorer (Eric Zimmerman) or Timesketch for visualization\n- Understanding of timestamp types (MACB: Modified, Accessed, Changed, Born)\n\n## Workflow\n\n### Step 1: Install Plaso and Prepare the Environment\n\n```bash\n# Install Plaso on Ubuntu\u002FDebian\nsudo add-apt-repository ppa:gift\u002Fstable\nsudo apt-get update\nsudo apt-get install plaso-tools\n\n# Or install via pip\npip install plaso\n\n# Or use Docker (recommended for dependency isolation)\ndocker pull log2timeline\u002Fplaso\n\n# Verify installation\nlog2timeline.py --version\npsort.py --version\n\n# Create output directory\nmkdir -p \u002Fcases\u002Fcase-2024-001\u002Ftimeline\u002F\n\n# Verify the forensic image\nimg_stat \u002Fcases\u002Fcase-2024-001\u002Fimages\u002Fevidence.dd\n```\n\n### Step 2: Generate the Plaso Storage File with log2timeline\n\n```bash\n# Basic processing of a disk image (all parsers)\nlog2timeline.py \\\n   --storage-file \u002Fcases\u002Fcase-2024-001\u002Ftimeline\u002Fevidence.plaso \\\n   \u002Fcases\u002Fcase-2024-001\u002Fimages\u002Fevidence.dd\n\n# Process with specific parsers for faster targeted analysis\nlog2timeline.py \\\n   --parsers \"winevtx,prefetch,mft,usnjrnl,lnk,recycle_bin,chrome_history,firefox_history,winreg\" \\\n   --storage-file \u002Fcases\u002Fcase-2024-001\u002Ftimeline\u002Fevidence.plaso \\\n   \u002Fcases\u002Fcase-2024-001\u002Fimages\u002Fevidence.dd\n\n# Process with a filter file to focus on specific paths\ncat \u003C\u003C 'EOF' > \u002Fcases\u002Fcase-2024-001\u002Ftimeline\u002Ffilter.txt\n\u002FWindows\u002FSystem32\u002Fwinevt\u002FLogs\n\u002FWindows\u002FPrefetch\n\u002FUsers\u002F*\u002FNTUSER.DAT\n\u002FUsers\u002F*\u002FAppData\u002FLocal\u002FGoogle\u002FChrome\n\u002FUsers\u002F*\u002FAppData\u002FRoaming\u002FMozilla\u002FFirefox\n\u002F$MFT\n\u002F$UsnJrnl:$J\n\u002FWindows\u002FSystem32\u002Fconfig\nEOF\n\nlog2timeline.py \\\n   --filter-file \u002Fcases\u002Fcase-2024-001\u002Ftimeline\u002Ffilter.txt \\\n   --storage-file \u002Fcases\u002Fcase-2024-001\u002Ftimeline\u002Fevidence.plaso \\\n   \u002Fcases\u002Fcase-2024-001\u002Fimages\u002Fevidence.dd\n\n# Using Docker\ndocker run --rm -v \u002Fcases:\u002Fcases log2timeline\u002Fplaso log2timeline \\\n   --storage-file \u002Fcases\u002Fcase-2024-001\u002Ftimeline\u002Fevidence.plaso \\\n   \u002Fcases\u002Fcase-2024-001\u002Fimages\u002Fevidence.dd\n\n# Process multiple evidence sources into one timeline\nlog2timeline.py \\\n   --storage-file \u002Fcases\u002Fcase-2024-001\u002Ftimeline\u002Fcombined.plaso \\\n   \u002Fcases\u002Fcase-2024-001\u002Fimages\u002Fworkstation.dd\n\nlog2timeline.py \\\n   --storage-file \u002Fcases\u002Fcase-2024-001\u002Ftimeline\u002Fcombined.plaso \\\n   \u002Fcases\u002Fcase-2024-001\u002Fimages\u002Fserver.dd\n```\n\n### Step 3: Filter and Export Timeline with psort\n\n```bash\n# Export full timeline to CSV (super-timeline format)\npsort.py \\\n   -o l2tcsv \\\n   -w \u002Fcases\u002Fcase-2024-001\u002Ftimeline\u002Ffull_timeline.csv \\\n   \u002Fcases\u002Fcase-2024-001\u002Ftimeline\u002Fevidence.plaso\n\n# Export with date range filter (focus on incident window)\npsort.py \\\n   -o l2tcsv \\\n   -w \u002Fcases\u002Fcase-2024-001\u002Ftimeline\u002Fincident_window.csv \\\n   \u002Fcases\u002Fcase-2024-001\u002Ftimeline\u002Fevidence.plaso \\\n   \"date > '2024-01-15 00:00:00' AND date \u003C '2024-01-20 23:59:59'\"\n\n# Export in JSON Lines format (for ingestion into SIEM\u002FTimesketch)\npsort.py \\\n   -o json_line \\\n   -w \u002Fcases\u002Fcase-2024-001\u002Ftimeline\u002Ftimeline.jsonl \\\n   \u002Fcases\u002Fcase-2024-001\u002Ftimeline\u002Fevidence.plaso\n\n# Export with specific source type filters\npsort.py \\\n   -o l2tcsv \\\n   -w \u002Fcases\u002Fcase-2024-001\u002Ftimeline\u002Fregistry_events.csv \\\n   \u002Fcases\u002Fcase-2024-001\u002Ftimeline\u002Fevidence.plaso \\\n   \"source_short == 'REG'\"\n\npsort.py \\\n   -o l2tcsv \\\n   -w \u002Fcases\u002Fcase-2024-001\u002Ftimeline\u002Fevtx_events.csv \\\n   \u002Fcases\u002Fcase-2024-001\u002Ftimeline\u002Fevidence.plaso \\\n   \"source_short == 'EVT'\"\n\n# Export for Timeline Explorer (dynamic CSV)\npsort.py \\\n   -o dynamic \\\n   -w \u002Fcases\u002Fcase-2024-001\u002Ftimeline\u002Ftimeline_explorer.csv \\\n   \u002Fcases\u002Fcase-2024-001\u002Ftimeline\u002Fevidence.plaso\n```\n\n### Step 4: Analyze Timeline with Timesketch\n\n```bash\n# Install Timesketch (Docker deployment)\ngit clone https:\u002F\u002Fgithub.com\u002Fgoogle\u002Ftimesketch.git\ncd timesketch\ndocker compose up -d\n\n# Import Plaso file into Timesketch via CLI\ntimesketch_importer \\\n   --host http:\u002F\u002Flocalhost:5000 \\\n   --username analyst \\\n   --password password \\\n   --sketch_id 1 \\\n   --timeline_name \"Case 2024-001 Workstation\" \\\n   \u002Fcases\u002Fcase-2024-001\u002Ftimeline\u002Fevidence.plaso\n\n# Alternatively, import JSONL\ntimesketch_importer \\\n   --host http:\u002F\u002Flocalhost:5000 \\\n   --username analyst \\\n   --sketch_id 1 \\\n   --timeline_name \"Case 2024-001\" \\\n   \u002Fcases\u002Fcase-2024-001\u002Ftimeline\u002Ftimeline.jsonl\n\n# In Timesketch web UI:\n# 1. Search for events: \"data_type:windows:evtx:record AND event_identifier:4624\"\n# 2. Apply Sigma analyzers for automated detection\n# 3. Star\u002Ftag important events\n# 4. Create stories documenting the investigation narrative\n# 5. Share with team members\n```\n\n### Step 5: Perform Targeted Timeline Analysis\n\n```bash\n# Analyze specific time periods around known events\npython3 \u003C\u003C 'PYEOF'\nimport csv\nfrom collections import defaultdict\nfrom datetime import datetime\n\n# Load incident window timeline\nevents_by_hour = defaultdict(list)\nsource_counts = defaultdict(int)\n\nwith open('\u002Fcases\u002Fcase-2024-001\u002Ftimeline\u002Fincident_window.csv', 'r', errors='ignore') as f:\n    reader = csv.DictReader(f)\n    total = 0\n    for row in reader:\n        total += 1\n        timestamp = row.get('datetime', row.get('date', ''))\n        source = row.get('source_short', row.get('source', 'Unknown'))\n        description = row.get('message', row.get('desc', ''))\n\n        source_counts[source] += 1\n\n        # Group by hour for activity patterns\n        try:\n            dt = datetime.strptime(timestamp[:19], '%Y-%m-%dT%H:%M:%S')\n            hour_key = dt.strftime('%Y-%m-%d %H:00')\n            events_by_hour[hour_key].append({\n                'time': timestamp,\n                'source': source,\n                'description': description[:200]\n            })\n        except (ValueError, TypeError):\n            pass\n\nprint(f\"Total events in incident window: {total}\\n\")\n\nprint(\"=== EVENTS BY SOURCE TYPE ===\")\nfor source, count in sorted(source_counts.items(), key=lambda x: x[1], reverse=True):\n    print(f\"  {source}: {count}\")\n\nprint(\"\\n=== ACTIVITY BY HOUR ===\")\nfor hour in sorted(events_by_hour.keys()):\n    count = len(events_by_hour[hour])\n    bar = '#' * min(count \u002F\u002F 10, 50)\n    print(f\"  {hour}: {count:>6} events {bar}\")\n\n# Find hours with unusual activity spikes\navg = total \u002F max(len(events_by_hour), 1)\nprint(f\"\\n=== ANOMALOUS HOURS (>{avg*3:.0f} events) ===\")\nfor hour in sorted(events_by_hour.keys()):\n    if len(events_by_hour[hour]) > avg * 3:\n        print(f\"  {hour}: {len(events_by_hour[hour])} events (SPIKE)\")\nPYEOF\n```\n\n## Key Concepts\n\n| Concept | Description |\n|---------|-------------|\n| Super-timeline | Unified chronological view combining all artifact timestamps from multiple sources |\n| MACB timestamps | Modified, Accessed, Changed (metadata), Born (created) - four key file timestamp types |\n| Plaso storage file | SQLite-based intermediate format storing parsed events before export |\n| L2T CSV | Log2timeline CSV format with standardized columns for timeline events |\n| Parser | Plaso module extracting timestamps from a specific artifact type (e.g., winevtx, prefetch) |\n| Psort | Plaso sorting and filtering tool for post-processing storage files |\n| Timesketch | Google open-source collaborative timeline analysis platform |\n| Pivot points | Known timestamps (e.g., malware execution) used to focus investigation scope |\n\n## Tools & Systems\n\n| Tool | Purpose |\n|------|---------|\n| log2timeline (Plaso) | Primary timeline generation engine parsing 100+ artifact types |\n| psort | Plaso output filtering, sorting, and export utility |\n| Timesketch | Web-based collaborative forensic timeline analysis platform |\n| Timeline Explorer | Eric Zimmerman's Windows GUI for CSV timeline analysis |\n| KAPE | Automated triage collection feeding into Plaso processing |\n| mactime (TSK) | Simpler timeline generation from Sleuth Kit bodyfiles |\n| Excel\u002FSheets | Manual timeline review for small filtered datasets |\n| Elastic\u002FKibana | Alternative visualization platform for JSONL timeline data |\n\n## Common Scenarios\n\n**Scenario 1: Ransomware Attack Reconstruction**\nProcess 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.\n\n**Scenario 2: Data Theft Investigation**\nCreate 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.\n\n**Scenario 3: Multi-System Breach Analysis**\nProcess 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.\n\n**Scenario 4: Insider Threat After-Hours Activity**\nFilter 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\u002Flegal.\n\n## Output Format\n\n```\nTimeline Reconstruction Summary:\n  Evidence Sources:\n    Disk Image: evidence.dd (500 GB, NTFS)\n    Plaso Storage: evidence.plaso (2.3 GB)\n\n  Processing Statistics:\n    Total events extracted: 4,567,890\n    Parsers used: 45 (winevtx, prefetch, mft, usnjrnl, lnk, chrome, firefox, winreg, ...)\n    Processing time: 3h 45m\n\n  Incident Window (2024-01-15 to 2024-01-20):\n    Events in window: 234,567\n    Event Sources:\n      MFT:          89,234\n      Event Logs:   45,678\n      USN Journal:  56,789\n      Registry:     23,456\n      Prefetch:     1,234\n      Browser:      5,678\n      LNK Files:    2,345\n      Other:        10,153\n\n  Key Timeline Events:\n    2024-01-15 14:32 - Phishing email opened (browser)\n    2024-01-15 14:33 - Malicious document downloaded\n    2024-01-15 14:35 - PowerShell executed (Prefetch + Event Log)\n    2024-01-15 14:36 - C2 connection established (Registry + Event Log)\n    2024-01-16 02:30 - Mimikatz execution (Prefetch)\n    2024-01-16 02:45 - Lateral movement to DC (Event Log)\n    2024-01-17 03:00 - Data exfiltration (MFT + USN Journal)\n    2024-01-18 03:00 - Log clearing (Event Log)\n\n  Exported Files:\n    Full Timeline:     \u002Ftimeline\u002Ffull_timeline.csv (4.5M rows)\n    Incident Window:   \u002Ftimeline\u002Fincident_window.csv (234K rows)\n    Timesketch Import: \u002Ftimeline\u002Ftimeline.jsonl\n```\n",{"name":5,"tags":43,"author":45,"domain":28,"license":14,"version":46,"subdomain":47,"description":7},[44,23,22,24,25,27],"forensics","mahipal","1.0","digital-forensics","master","3f5d983eb4ef375b749adcd6b07ec03bd82ae8a8","_universal\u002Fcybersecurity-skills\u002Fskills\u002Fperforming-timeline-reconstruction-with-plaso",0,"https:\u002F\u002Fgithub.com\u002Fmicsapp\u002Fmicstec-skills","https:\u002F\u002Fgithub.com\u002Fmicsapp","2026-03-20 11:24:18",{"source":8,"skillName":5,"commands":56,"prompt":66,"zipAvailable":67},[57,60,63],{"tool":58,"command":59},"npx","npx skills add micsapp\u002Fmicstec-skills --skill performing-timeline-reconstruction-with-plaso",{"tool":61,"command":62},"bunx","bunx skills add micsapp\u002Fmicstec-skills --skill performing-timeline-reconstruction-with-plaso",{"tool":64,"command":65},"pnpm","pnpm dlx skills add micsapp\u002Fmicstec-skills --skill performing-timeline-reconstruction-with-plaso","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fmicsapp\u002Fmicstec-skills\u002Fblob\u002Fmaster\u002F_universal\u002Fcybersecurity-skills\u002Fskills\u002Fperforming-timeline-reconstruction-with-plaso\u002FSKILL.md",true,[69,74,80],{"path":70,"resourceType":70,"resourceTypeDesc":71,"sizeBytes":72,"sourceHtmlUrl":73},"LICENSE","许可证",11255,"https:\u002F\u002Fgithub.com\u002Fmicsapp\u002Fmicstec-skills\u002Fblob\u002Fmaster\u002F_universal\u002Fcybersecurity-skills\u002Fskills\u002Fperforming-timeline-reconstruction-with-plaso\u002FLICENSE",{"path":75,"resourceType":76,"resourceTypeDesc":77,"sizeBytes":78,"sourceHtmlUrl":79},"references\u002Fapi-reference.md","REFERENCE","参考文档",2575,"https:\u002F\u002Fgithub.com\u002Fmicsapp\u002Fmicstec-skills\u002Fblob\u002Fmaster\u002F_universal\u002Fcybersecurity-skills\u002Fskills\u002Fperforming-timeline-reconstruction-with-plaso\u002Freferences\u002Fapi-reference.md",{"path":81,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":84,"sourceHtmlUrl":85},"scripts\u002Fagent.py","SCRIPT","脚本",6872,"https:\u002F\u002Fgithub.com\u002Fmicsapp\u002Fmicstec-skills\u002Fblob\u002Fmaster\u002F_universal\u002Fcybersecurity-skills\u002Fskills\u002Fperforming-timeline-reconstruction-with-plaso\u002Fscripts\u002Fagent.py",[87,125,155,176,196,220],{"skillNo":88,"name":89,"nameZh":6,"description":90,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":91,"licenseFileUrl":6,"sourceHtmlUrl":92,"categories":93,"keywords":104,"publishedAt":38},"01M6HBTJ02FXBFG9NYEHHYAP52","azure-apim-architecture","Analyzes and explains Azure API Management architecture decisions for enterprise API marketplace implementations using VNet Internal mode, Front Door, hybrid authentication, and multi-environment strategies. Use when discussing APIM component selection, network topology, cost optimization, or comparing alternatives like workspaces vs instances, VNet Internal vs External mode, or Front Door vs Application Gateway.","MIT","https:\u002F\u002Fgithub.com\u002Fmicsapp\u002Fmicstec-skills\u002Fblob\u002Fmaster\u002Fcopilot-cli\u002Fcopilot-agent-skills\u002F.github\u002Fskills\u002Fazure-apim-architecture\u002FSKILL.md",[94,99],{"categoryNo":95,"categoryName":96,"taxonomyType":97,"taxonomyTypeDesc":98},"01JBSEEDCAT000000000000010","后端与接口","SCENARIO","使用场景",{"categoryNo":100,"categoryName":101,"taxonomyType":102,"taxonomyTypeDesc":103},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124],"architecture","azure","azure apim architecture","component","authentication","gateway","topology","Optimization","network","api","team","environment","multi-environment","workspaces","instances","selection","strategies","azure api management","mode","for",{"skillNo":126,"name":127,"nameZh":6,"description":128,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":91,"licenseFileUrl":6,"sourceHtmlUrl":129,"categories":130,"keywords":139,"publishedAt":38},"01M6HBT7J4H082JEEA670B3EQ9","apiops-deployment","Guides deployment of Azure API Management infrastructure using Infrastructure as Code (Bicep\u002FTerraform), CI\u002FCD pipelines (GitHub Actions\u002FAzure DevOps), and APIOps workflows. Use when deploying APIM, creating pipelines, or implementing dev→test→prod promotion strategies.","https:\u002F\u002Fgithub.com\u002Fmicsapp\u002Fmicstec-skills\u002Fblob\u002Fmaster\u002Fcopilot-cli\u002Fcopilot-agent-skills\u002F.github\u002Fskills\u002Fapiops-deployment\u002FSKILL.md",[131,132,135,138],{"categoryNo":95,"categoryName":96,"taxonomyType":97,"taxonomyTypeDesc":98},{"categoryNo":133,"categoryName":134,"taxonomyType":97,"taxonomyTypeDesc":98},"01JBSEEDCAT000000000000011","运维与部署",{"categoryNo":136,"categoryName":137,"taxonomyType":97,"taxonomyTypeDesc":98},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":100,"categoryName":101,"taxonomyType":102,"taxonomyTypeDesc":103},[140,141,142,143,106,144,145,146,147,148,149,150,114,115,121,151,152,122,153,154],"deployment","apiops deployment","workflows","azure devops","infrastructure","code","infrastructure as code","actions","ci","github actions","github-actions","as","guides","CI\u002FCD","implementing",{"skillNo":156,"name":157,"nameZh":6,"description":158,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":91,"licenseFileUrl":6,"sourceHtmlUrl":159,"categories":160,"keywords":162,"publishedAt":38},"01M6HBT2HEA23THNE9E00GYQBE","apim-policy-authoring","Creates production-ready Azure API Management policy XML for authentication (OAuth 2.0, JWT validation, subscription keys), rate limiting, CORS configuration, error handling, and API transformations. Use when implementing API security, access control, or request\u002Fresponse processing logic.","https:\u002F\u002Fgithub.com\u002Fmicsapp\u002Fmicstec-skills\u002Fblob\u002Fmaster\u002Fcopilot-cli\u002Fcopilot-agent-skills\u002F.github\u002Fskills\u002Fapim-policy-authoring\u002FSKILL.md",[161],{"categoryNo":95,"categoryName":96,"taxonomyType":97,"taxonomyTypeDesc":98},[163,164,165,166,106,109,114,115,167,168,169,170,171,172,173,122,174,175,154,124],"policy","authoring","apim policy authoring","security","access","use","ready","api security","Access Control","error","processing","cors configuration","production-ready",{"skillNo":177,"name":178,"nameZh":6,"description":179,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":91,"licenseFileUrl":6,"sourceHtmlUrl":180,"categories":181,"keywords":184,"publishedAt":38},"01M6HBSY65SB29J9WE9KYD0K6T","api-security-review","Reviews Azure API Management configurations for security vulnerabilities, OWASP API Security Top 10 compliance, VNet Internal mode validation, Private Link verification, and Azure Security Benchmark alignment. Use when performing security audits, pre-deployment validation, or compliance reviews.","https:\u002F\u002Fgithub.com\u002Fmicsapp\u002Fmicstec-skills\u002Fblob\u002Fmaster\u002Fcopilot-cli\u002Fcopilot-agent-skills\u002F.github\u002Fskills\u002Fapi-security-review\u002FSKILL.md",[182,183],{"categoryNo":95,"categoryName":96,"taxonomyType":97,"taxonomyTypeDesc":98},{"categoryNo":100,"categoryName":101,"taxonomyType":102,"taxonomyTypeDesc":103},[185,166,186,114,170,187,106,140,188,189,190,191,115,192,122,193,194,195,123,124],"review","security review","api security review","benchmark","verification","owasp","owasp-api","alignment","azure security","reviews","top",{"skillNo":197,"name":198,"nameZh":6,"description":199,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":91,"licenseFileUrl":6,"sourceHtmlUrl":200,"categories":201,"keywords":202,"publishedAt":38},"01M6HBSRW4PDFE6K4F8JYPZQ05","jira-cli","Interact with Jira from the command line to create, list, view, edit, and transition issues, manage sprints and epics, and perform common Jira workflows. Use when the user asks about Jira tasks, tickets, issues, sprints, or needs to manage project work items.","https:\u002F\u002Fgithub.com\u002Fmicsapp\u002Fmicstec-skills\u002Fblob\u002Fmaster\u002Fcopilot-cli\u002Fawesome-copilot-agents\u002Fskills\u002Fjira-cli\u002FSKILL.md",[],[203,204,205,142,206,207,208,209,114,210,211,212,213,214,31,215,216,217,218,219],"cli","jira","jira cli","init","tasks","create","issues","common","work","command","edit","list","project","project work","token","line","sprints",{"skillNo":221,"name":222,"nameZh":6,"description":223,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":91,"licenseFileUrl":6,"sourceHtmlUrl":224,"categories":225,"keywords":226,"publishedAt":38},"01M6HBSNS3JHMW603G47TWE31H","calculator","Performs arbitrary-precision arithmetic calculations including addition, subtraction, multiplication, division, and exponents. Use when the user asks to calculate, compute, or evaluate math expressions, or when precise decimal arithmetic is needed to avoid floating-point errors.","https:\u002F\u002Fgithub.com\u002Fmicsapp\u002Fmicstec-skills\u002Fblob\u002Fmaster\u002Fcopilot-cli\u002Fawesome-copilot-agents\u002Fskills\u002Fcalculator\u002FSKILL.md",[],[222,227,228,229,230,231,232,168,233,234],"npm","js","calculate","node","math","user","compute","errors"]