Network Activity Forensics - SOC Analyst Cheatsheet
Practical Guide for Network Connection & Usage Investigation
Quick Reference: Network Artifacts Matrix
Network List
Networks connected
Historical
SSIDs, first/last connect
Both
Low
TCP/IP Interfaces
IP addresses
Current + recent
IPs, gateways, DNS
Both
Medium
SRUM
Data usage, apps
30-60 days
Bytes sent/received, per-app
Both
Low
Investigation Priority Matrix
CRITICAL
SRUM
Data exfiltration, app usage
Win8+
Bytes sent/received per app
HIGH
Network List
Wi-Fi history, VPN
All Windows
Connection timeline
MEDIUM
TCP/IP Interfaces
Current/recent IPs
All Windows
Active network config
Core Investigation Questions
Primary Questions:
What networks has the device connected to? (Wi-Fi, VPN history)
What is the current/last IP address? (Network configuration)
Which applications used the network heavily? (Data exfiltration)
Secondary Questions:
When were networks first/last used? (Timeline)
How much data was sent/received? (Volume analysis)
Were VPN connections used? (Anonymisation attempts)
SOC Investigation Workflows
Workflow 1: Data Exfiltration Investigation (CRITICAL)
Scenario: Suspected data theft via network transfer
Investigation Priority Order:
Step 1: Analyse SRUM (Network Data Usage) - CRITICAL Why first: Shows bytes sent/received per application (smoking gun for data exfil)
Location:
Required Companion File:
Collection Commands:
PowerShell - Copy SRUM and SOFTWARE:
Using SrumECmd (Zimmerman Tool) - REQUIRED:
Critical SRUM Tables:
1. Network Data Usage Table
2. Application Resource Usage Table
3. Network Connectivity Usage Table
Analysis - Data Exfiltration Detection:
PowerShell - Analyze SRUM CSV (After Parsing):
Red Flags in SRUM:
High Data Upload Indicators:
β High upload ratio (sent >> received) β Data exfiltration
β Unusual applications with high network usage
β Applications from temp directories using network
β Non-browser apps with massive uploads
β Compression tools (7z, WinRAR) with network usage
β Unknown executables with high bandwidth
Typical Patterns:
Normal (High Download, Low Upload):
Suspicious (High Upload):
Data Exfiltration Tools in SRUM:
Step 2: Check Network Connection History Why second: Identifies what networks were used (including VPN)
Registry Location:
PowerShell - Parse Network List:
Network List Forensic Value:
Network names (SSIDs)
First connection timestamp
Last connection timestamp
Managed vs. Unmanaged (corporate vs. public)
VPN connections appear as network entries
Red Flags in Network List:
β VPN connections during incident timeframe
β Public/unknown Wi-Fi on corporate device
β Personal hotspot connections
β Connection timing correlates with exfiltration
β New networks appearing during investigation period
Step 3: Identify Current/Recent IP Configuration Why third: Shows IP addresses used
Registry Location:
PowerShell - Enumerate Network Interfaces:
TCP/IP Forensic Value:
Current and historical IP addresses
DHCP vs. static configuration
Default gateway (router)
DNS server configuration
DHCP server addresses
Red Flags in Network Config:
β Unusual DNS servers (attacker DNS, privacy DNS)
β Suspicious gateways (man-in-the-middle)
β IP changes during incident window
β VPN interface IP addresses
PowerShell Script: Complete Network Activity Investigation
Workflow 2: VPN & Anonymisation Detection
Scenario: Detect use of VPN or anonymisation tools
Investigation Steps:
Step 1: Check Network List for VPN Connections
PowerShell - Detect VPN Networks:
Step 2: Check SRUM for VPN Application Usage
Step 3: Correlate VPN Usage with Data Transfer
Workflow 3: Baseline vs. Anomaly Detection
Scenario: Identify abnormal network usage patterns
Analysis Technique:
Compare Application Network Usage:
PowerShell - Baseline Analysis:
Detection Patterns & Red Flags
Data Exfiltration Indicators
SRUM Patterns:
VPN/Anonymisation Usage
Network List Indicators:
SRUM Indicators:
Lateral Movement Detection
SRUM Indicators:
Common Investigation Scenarios
Scenario 1: Cloud Storage Exfiltration
Evidence Chain:
Analysis:
Scenario 2: VPN-Based Data Theft
Evidence Chain:
Timeline Correlation:
Scenario 3: Insider Threat Baseline Deviation
Evidence Chain:
SRUM Analysis Deep Dive
Understanding SRUM Data
Recording Interval:
Data recorded approximately every hour
Cumulative counters (total since boot)
Requires aggregation for analysis
Table Relationships:
Key Metrics:
BytesSent:
Total bytes uploaded by application
Cumulative counter
Resets on system reboot
BytesRecvd:
Total bytes downloaded by application
Cumulative counter
Resets on system reboot
Upload Ratio Calculation:
SRUM CSV Output Columns Reference
NetworkUsage.csv:
Timestamp- Recording timeApp- Application pathUserId- User SIDBytesSent- Bytes uploadedBytesRecvd- Bytes downloadedInterfaceLuid- Network interface IDL2ProfileId- Network profile IDL2ProfileFlags- Profile flags
AppResourceUseInfo.csv:
Timestamp- Recording timeAppId- Application IDUserId- User SIDForegroundCycleTime- CPU time (foreground)BackgroundCycleTime- CPU time (background)FaceTime- Face detection timeForegroundContextSwitches- Context switchesBackgroundContextSwitches- Context switchesForegroundBytesRead- Disk read (foreground)ForegroundBytesWritten- Disk write (foreground)
NetworkConnections.csv:
Timestamp- Recording timeAppId- Application IDUserId- User SIDInterfaceLuid- Network interfaceL2ProfileId- Network profileConnectedTime- Connection duration (seconds)ConnectStartTime- Connection start
Tools & Commands Reference
SrumECmd (Eric Zimmerman)
Basic Usage:
Output Files:
Analysis Priority:
NetworkUsage.csv - Primary for data exfiltration
AppResourceUseInfo.csv - Context (app was running)
NetworkConnections.csv - Connection duration
Registry Queries
Network List:
TCP/IP Interfaces:
Live Network Commands
Investigation Checklists
Data Exfiltration Investigation
[ ] Collect SRUM database (SRUDB.dat)
[ ] Collect SOFTWARE hive for app resolution
[ ] Parse with SrumECmd
[ ] Analyse NetworkUsage CSV
[ ] Sort by BytesSent (descending)
[ ] Calculate upload ratios
[ ] Identify applications with >50% upload ratio
[ ] Check for unusual application paths
[ ] Correlate with execution artifacts (Prefetch/BAM)
[ ] Check Network List for VPN usage
[ ] Build timeline of high upload events
[ ] Cross-reference with file access artifacts
VPN Detection Investigation
[ ] Parse Network List registry
[ ] Search for VPN keywords (OpenVPN, NordVPN, etc.)
[ ] Check first/last connection times
[ ] Parse SRUM for VPN client executables
[ ] Correlate VPN connection with data uploads
[ ] Check browser history for VPN provider sites
[ ] Review execution artifacts for VPN installers
[ ] Document VPN usage timeline
Network Baseline Investigation
[ ] Parse SRUM for full 30-60 day history
[ ] Calculate average daily usage per application
[ ] Identify maximum usage per application
[ ] Detect anomalies (>3x average)
[ ] Focus on anomalies during incident window
[ ] Correlate with user activity timeline
[ ] Document deviation patterns
Best Practices
SRUM Collection
β DO:
Collect both SRUDB.dat and SOFTWARE hive
Collect .LOG files (transaction logs)
Parse offline (don't open database live)
Hash files before analysis
Document collection timestamp
β DON'T:
Open SRUDB.dat without SOFTWARE hive (app names won't resolve)
Modify database during collection
Skip transaction logs
Forget OS version check (Win8+ only)
Analysis Methodology
β DO:
Start with NetworkUsage.csv
Calculate upload ratios
Look for anomalies in app paths
Correlate with execution artifacts
Build timeline of network events
Cross-reference multiple artifacts
β DON'T:
Rely solely on SRUM
Ignore low upload volumes (persistence C2)
Skip baseline comparison
Forget timezone conversions
Limitations & Caveats
SRUM Limitations
Network List Limitations
TCP/IP Limitations
Summary: Critical Takeaways
Artifact Strengths
SRUM:
β Best for: Data exfiltration detection
β Shows: Bytes sent/received per application
β Retention: 30-60 days
β Limitation: Windows 8+ only
Network List:
β Best for: Wi-Fi/VPN history
β Shows: Networks connected, first/last times
β Retention: Persistent
β Limitation: No bandwidth data
TCP/IP Interfaces:
β Best for: IP address history
β Shows: Current and recent IPs
β Retention: Recent only
β Limitation: No historical timeline
Investigation Strategy
Check SRUM first (data exfiltration smoking gun)
Analyse upload ratios (sent vs. received)
Identify suspicious apps (temp paths, unknown)
Check Network List (VPN usage)
Review TCP/IP config (IP addresses)
Correlate with execution artifacts (validate findings)
Key Principle
SRUM is the gold standard for data exfiltration detection on Windows 8+. High upload ratios combined with unusual applications provide strong evidence of data theft.
Remember: SRUM's bytes sent/received per application is your best evidence for data exfiltration. Calculate upload ratios and look for anomaliesβnormal applications download more than they upload!
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