This case study presents a forensic investigation of a sophisticated malware attack on a client’s network. Using  open-source tools, an in-depth memory forensics analysis was performed to identify malware, suspicious processes, and evidence of compromise. The incident posed significant challenges, particularly due to the advanced evasion techniques used by the attackers.

Overview of the Incident

Background

The client, a small healthcare organization, experienced unexplained network disruptions and data integrity issues. Unusual network traffic indicated potential data exfiltration. Endpoint protection systems provided limited insight, requiring a more thorough forensic analysis, particularly of live memory.

Challenges with this case

1. Advanced Evasion Tactics:
   • The attackers utilized fileless malware, leveraging memory-resident code to avoid disk-based detection.
   • Obfuscation techniques, such as polymorphic code and in-memory-only payloads, complicated forensic analysis.
     
2. Heavy Network Load:
   Continuous network operations with minimal downtime made it challenging to safely capture memory and system images without disrupting services.
   
3. Data Confidentiality:
   Highly sensitive patient data necessitated rigorous compliance with data security protocols during the investigation.

Investigation

Phase 1: Identification and Preliminary Actions

Immediate Actions:

• Isolated compromised systems from the network to prevent further data exfiltration.
• Utilized Magnet RAM Capture and DumpIt to acquire volatile memory images from affected machines quickly.
  
  

Toolset Selection:

• Chose Volatility as the primary memory forensics framework due to its comprehensive support for in-depth memory analysis.
• Used Redline to supplement memory image extraction and provide detailed audit logs.

Phase 2: Identifying Malware and Suspicious Processes

Tools Used:

• Volatility: Core tool for all memory analysis tasks.
• Redline: Assisted in organizing initial data and identifying high-level indicators of compromise.

Key Findings:

• Rogue Processes:
  1. Initial process scan (pslist plugin) revealed several unusual processes running without corresponding entries in the legitimate installed programs list.
  2. Detected a process svchost.exe with inconsistent parent-child relationships (often a sign of process injection).

• DLL Injection:
  1. Used the dlllist and ldrmodules plugins to compare loaded DLLs within suspicious processes.
  2. Found unauthorized DLLs loaded in explorer.exe, executing from non-standard directories (e.g., C:\Temp\).

• Code Injection Indicators:
  The malfind plugin pinpointed areas of injected code by identifying memory regions marked as both executable and writable – a strong indicator of injected, malicious code.

2. Network Artifact Analysis

Tools Used:

Volatility with network-related plugins (netscan, connscan).

Key Findings:

Active Connections:

• netscan plugin unveiled active network connections from non-standard ports, communicating with IPs linked to known malicious C2 servers.
• connscan identified historical network connections, providing insights into past communications with external servers involved in data exfiltration.

3. Execution Artifacts Analysis

Tools Used:

• Volatility plugins: cmdscan, consoles, and cmdline.

Key Findings:

Command History:

• cmdscan and consoles plugins revealed command execution history, including PowerShell commands that downloaded further payloads and established reverse shells.
• Discovered commands used for privilege escalation and lateral movement across the network.

Evidence of Persistence:

• autoruns and svcscan plugins identified registry entries and services that ensured malware persistence after reboots.

Phase 3: Threat Attribution

Analysis and Correlation:

Data Cross-Referencing:

• Cross-referenced findings with known IOCs using open-source threat intelligence platforms.
• Used network connection logs and injected DLL hashes to correlate with threat actor TTPs from the MITRE ATT&CK framework.

Attribution Techniques:

Conducted open source intelligence activities to trace malicious IP addresses and domain registrants, revealing ties to previously documented APT groups.

Mitigation and Clean-Up

1. Host-Based Clean-Up:
   • Utilized identified memory artifacts to cleanse infected processes, remove malicious injected code, and restore compromised systems.
   • Applied remediation scripts to remove registry-persistence mechanisms and unauthorized services.

2. Network Improvements:
   • Implemented stricter firewall rules and network segmentation to prevent future unauthorized communications.
   • Deployed additional monitoring using open-source tools like Snort (I also recommended Zeek) for better anomaly detection.

3. Security Enhancements:
   • Enforced strict application whitelisting and constrained usage of PowerShell.
   • Rolled out enhanced logging policies to capture more granular events across the network.

Lessons Learned

1. Real-Time Memory Forensics Importance:
   Analyzing memory is crucial for detecting and understanding complex memory-based malwares.

2. Artifact Correlation:
   A complete understanding of an attack requires correlating multiple data sources, such as network artifacts, process execution history, and the locations of injected code.

Outcome

The investigation improved the client’s overall security posture, successfully eliminated the persistent threat, and put the network back in a secure state. All this was done without diminishing the quality and effectiveness of forensic analysis, by using open-source tools.

This case study describes how, despite resource constraints and sophisticated threats, memory forensics can effectively detect malware, suspicious processes, and evidence of compromise.