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Network Intrusion Analysis - Part 2: Forensic Strategies to Trace Leak's Origin(potd)

Useful techniques for a quick and effective intervention

Digital Data Leak Cybersecurity Breach

Digital Data Leak Cybersecurity Breach

To trace the origin of a suspected insider leak of confidential information via encrypted channels, a multi-faceted forensic strategy is essential. Since the traffic is encrypted, direct content inspection is not feasible, but analyzing patterns, anomalies, and contextual data can reveal the source. Below are recommended forensic strategies to identify the insider:

1. Network Traffic Analysis

Since the content of encrypted traffic cannot be inspected, focus on metadata and patterns to detect suspicious activity.

Flow Analysis

Use tools like NetFlow or sFlow to examine summaries of network traffic:

  • Identify large or frequent data transfers from internal IP addresses to external destinations.
  • Check for connections to unusual, non-business-related IP addresses or domains.
  • Look for data transfers at odd hours or in patterns inconsistent with normal business operations.

Packet Metadata Analysis

With tools like Wireshark, analyze packet metadata:

  • Examine packet sizes (e.g., consistently large sizes may indicate bulk data transfers).
  • Study timing and frequency (e.g., regular intervals could suggest automated exfiltration).
  • Detect encrypted traffic on non-standard ports (e.g., not 443 for HTTPS) or to unexpected destinations, which might indicate covert channels.

2. Endpoint Analysis

Investigate the devices potentially involved in the leak to uncover unauthorized activities.

Process and Software Audit

On suspected endpoints:

  • Check for unauthorized processes or software (e.g., encryption tools like OpenSSL or Socat).
  • Look for scripts or scheduled tasks that could automate data transfers.

File Access and Transfer Monitoring

  • Review file access logs to identify users who accessed sensitive files.
  • Search for evidence of data being copied to external drives or uploaded to cloud services.

Memory and Disk Forensics

  • Use tools like Volatility for memory analysis to detect running processes or encryption keys.
  • Perform disk forensics to recover deleted files or temporary data related to the leak.

3. Log Correlation and Event Timeline

Logs provide critical clues about user activities and can help build a timeline of events.

System and Application Logs

Analyze logs from:

  • User login and activity records to pinpoint who was active during suspicious traffic times.
  • Firewall and proxy logs to track outbound connections.
  • VPN or remote access logs if the insider might be operating remotely.

Event Correlation

Combine data to create a timeline:

  • Match network traffic spikes with user login times.
  • Correlate file access events with data transfer patterns.
  • Identify anomalies in user behavior (e.g., accessing files outside their typical role).

4. Insider Behavior Analysis

Detect deviations from normal user behavior to narrow down suspects.

User Behavior Analysis (UBA)

If available, use UBA tools to identify:

  • Unusual login times or locations.
  • Access to sensitive data not typically required for the user’s role.
  • Elevated activity during times of suspicious traffic.

Insider Threat Indicators

Look for contextual signs such as:

  • Disgruntled employees or recent terminations.
  • Users with elevated privileges who might have easier access to sensitive data.

5. Honeypots and Decoys

Proactively trap the insider by monitoring fake sensitive data.

Deploy Decoy Files

  • Place fake sensitive files (honeypots) in accessible locations.
  • Monitor for access or exfiltration attempts.

Track Access

  • If an insider interacts with these files, it can directly point to the source of the leak.

Conclusion

To effectively trace the origin of a data leak, combine these strategies:

  • Network traffic analysis to spot suspicious patterns.
  • Endpoint forensics to uncover unauthorized activities on devices.
  • Log correlation to build an event timeline.
  • Behavioral analysis to detect anomalies in user actions.
  • Honeypots to proactively identify the leaker.

This multi-layered approach maximizes the chances of pinpointing the insider while reducing false positives. By systematically applying these techniques to the network traffic logs and related data, you can identify the source of the leak and take appropriate action.

Credits

  • Dr BUGINGO Emmanuel, our lecturer who allowed us to develop this prototype
  • Alain MUGISHA, Group 12 member
  • Matheo OKISSI, Group 12 member

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