njRAT: Network Detection, Behavioral Analysis, and Validation (Part 3/3)

Executive Summary

This final blog in the series focuses on advanced network detection techniques, cross-variant behavioral analysis, and validation strategies for njRAT detection rules. We provide field-ready simulation plans and operational guidance for SOC teams.

High-Fidelity Network Detection for njRAT

JA3/JA3S Fingerprints for njRAT Variants

Collection Point: Network traffic capture at SPAN port or proxy logs.

Detection Logic:

• Capture JA3/JA3S fingerprints from TLS handshake packets.
• Compare against known njRAT fingerprints from threat intelligence feeds.

Resilient Patterns:

• JA3 fingerprints remain consistent across different IPs/domains.
• Monitor for specific JA3 hashes associated with njRAT variants.

Correlation Opportunities:

• Cross-reference with host-based telemetry for process initiating the connection.

URI Patterns and HTTP/HTTPS Communication

Collection Point: Proxy logs or deep packet inspection (DPI) systems.

Detection Logic:

• Identify consistent URI patterns used by njRAT for C2 communication.
• Monitor for specific HTTP headers or user-agent strings indicative of njRAT.

Resilient Patterns:

• URI structures often remain unchanged despite domain/IP changes.
• Look for repetitive or unusual URI requests.

Correlation Opportunities:

• Match with process execution logs to identify the originating application.

TLS Handshake Anomalies and Certificate Patterns

Collection Point: TLS handshake logs from network appliances or DPI.

Detection Logic:

• Analyze certificate details for anomalies such as self-signed certificates.
• Monitor for unusual cipher suites or TLS versions.

Resilient Patterns:

• Certificate anomalies persist across different infrastructure.
• Consistent use of specific cipher suites by njRAT.

Correlation Opportunities:

• Validate against host-based certificate stores for unexpected certificates.

Packet Size and Timing Profiles

Collection Point: Netflow data or packet capture at SPAN port.

Detection Logic:

• Analyze packet size distributions and timing intervals for C2 traffic.
• Identify patterns of small, regular packets indicative of njRAT.

Resilient Patterns:

• Consistent packet size and timing profiles despite IP/domain changes.
• Look for periodic beaconing behavior.

Correlation Opportunities:

• Correlate with system activity logs to identify idle periods matching beaconing.

DNS Patterns and Domain Generation Algorithms

Collection Point: DNS query logs from DNS servers or network appliances.

Detection Logic:

• Monitor for specific DNS query patterns or DGA-related anomalies.
• Identify frequent DNS requests to newly registered or low-reputation domains.

Resilient Patterns:

• DGA patterns remain consistent across different campaigns.
• High frequency of DNS queries to similar domain structures.

Correlation Opportunities:

• Cross-reference with host DNS cache for persistence of queried domains.

C2 Communication Protocols and Encryption

Collection Point: Network traffic capture at SPAN port or DPI systems.

Detection Logic:

• Identify specific protocol anomalies or encryption methods used by njRAT.
• Monitor for unusual traffic patterns on non-standard ports.

Resilient Patterns:

• Consistent use of specific encryption methods or protocol deviations.
• Protocol anomalies persist despite infrastructure changes.

Correlation Opportunities:

• Match with host-based process logs for applications using non-standard protocols.

Data Exfiltration Patterns and Methods

Collection Point: Netflow data or packet capture at SPAN port.

Detection Logic:

• Detect large data transfers to external IPs or domains.
• Monitor for unusual outbound traffic volumes or patterns.

Resilient Patterns:

• Consistent data exfiltration methods across different campaigns.
• Look for specific file types or data structures in outbound traffic.

Correlation Opportunities:

• Correlate with file access logs to identify potential data sources.

Port Scanning and Network Discovery Patterns

Collection Point: Network intrusion detection systems (NIDS) or netflow data.

Detection Logic:

• Identify scanning patterns such as SYN scans or unusual port access attempts.
• Monitor for lateral movement attempts within the network.

Resilient Patterns:

• Consistent scanning techniques used by njRAT operators.
• Look for repeated access attempts to specific network segments.

Correlation Opportunities:

• Cross-reference with host firewall logs for blocked or allowed connections.

Cross-Variant Behavioral Anchors

Unusual Windows API Sequences Used by njRAT

API Call Sequences and Patterns

• njRAT variants often utilize a sequence of API calls for process injection and keylogging, including OpenProcess, VirtualAllocEx, WriteProcessMemory, and CreateRemoteThread.
• Detection Logic: Monitor for unusual sequences of these API calls, especially when originating from non-standard processes or those lacking administrative privileges.
• Baseline Establishment: Establish a baseline of typical API call sequences for legitimate applications to identify deviations indicative of njRAT activity.

Parent-Child Process Anomalies During Credential Theft

Process Relationship Anomalies

• njRAT frequently spawns child processes from unexpected parent processes, such as explorer.exe or svchost.exe, to perform credential theft.
• Detection Logic: Identify and alert on atypical parent-child process relationships, particularly those involving credential-related processes like lsass.exe.
• Baseline Establishment: Map normal parent-child process hierarchies to detect anomalies.

File System Access Patterns to Specific Browser Profile Directories

File System Access Patterns

• njRAT variants access browser profile directories, such as those of Chrome (%LOCALAPPDATA%\Google\Chrome\User Data\) and Firefox (%APPDATA%\Mozilla\Firefox\Profiles\), to exfiltrate stored credentials.
• Detection Logic: Monitor for unauthorized access to these directories, especially by processes not typically associated with browser operations.
• Baseline Establishment: Define normal access patterns to browser profile directories to identify deviations.

Memory Allocation Patterns for Process Injection

Memory Allocation and Injection Patterns

• njRAT uses specific memory allocation patterns, such as allocating executable memory regions using VirtualAllocEx followed by WriteProcessMemory.
• Detection Logic: Detect memory allocations with PAGE_EXECUTE_READWRITE permissions in processes that do not usually perform code injection.
• Baseline Establishment: Profile legitimate memory allocation behaviors to distinguish malicious injections.

Registry Access Patterns for Persistence

Registry Modification Patterns

• njRAT often modifies registry keys for persistence, such as HKCU\Software\Microsoft\Windows\CurrentVersion\Run.
• Detection Logic: Monitor for changes to common persistence-related registry keys, especially by processes without installation privileges.
• Baseline Establishment: Document standard registry modifications for persistence to identify unauthorized changes.

Network Communication Timing and Patterns

Network Communication Behavioral Signatures

• njRAT exhibits specific network communication patterns, including periodic beaconing to command and control (C2) servers and unusual port usage.
• Detection Logic: Analyze network traffic for consistent intervals of outbound connections and connections to known C2 IP addresses or domains.
• Baseline Establishment: Establish normal network communication patterns to detect anomalies indicative of njRAT activity.

Anti-Forensics and Evasion Technique Detection

Anti-Forensics and Evasion Techniques

• njRAT employs techniques such as disabling security tools and using obfuscation to evade detection.
• Detection Logic: Monitor for attempts to disable or modify security software processes and services, as well as the presence of obfuscated scripts or binaries.
• Baseline Establishment: Identify standard security tool configurations and operations to detect unauthorized modifications.

Keylogging and Screen Capture Behavioral Patterns

Keylogging and Data Collection Patterns

• njRAT captures keystrokes and screen data using specific API calls like GetAsyncKeyState and BitBlt.
• Detection Logic: Detect the use of these API calls by processes not typically associated with input or screen capture functionalities.
• Baseline Establishment: Profile legitimate use of input and screen capture APIs to identify suspicious activities.

Validation & Simulation

Simulation Plan for njRAT Detection

Tools and Frameworks

• Atomic Red Team: Utilize for simulating common njRAT behaviors.
• Custom Benign Simulators: Develop scripts to mimic njRAT network and file activities without malicious payloads.
• Sysmon: For detailed process creation and network connection logging.
• Wireshark: To capture and analyze network traffic patterns.

Simulation Commands and Procedures

1. Process Creation Simulation

• Objective: Simulate njRAT process creation.
• Command: Use PowerShell to create a benign process that mimics njRAT execution.

Start-Process -FilePath "C:\Windows\System32\notepad.exe" -ArgumentList "/c echo njRAT simulation" -WindowStyle Hidden

• Validation: Ensure the SOC pipeline detects the process creation event with the correct parent-child relationship.

1. Registry Modification Simulation

• Objective: Simulate registry changes typical of njRAT persistence mechanisms.
• Command: Use reg.exe to add a benign registry key.

reg add HKCU\Software\Microsoft\Windows\CurrentVersion\Run /v njRATSim /t REG_SZ /d "C:\Windows\System32\notepad.exe"

• Validation: Confirm detection of registry modification events.

1. Network Activity Simulation

• Objective: Simulate njRAT network communication.
• Command: Use curl to simulate outbound connections.

curl http:///njrat-simulation

• Validation: Verify detection of unusual outbound network traffic.

1. File Dropping Simulation

• Objective: Simulate file creation typical of njRAT payloads.
• Command: Use PowerShell to create a benign file.

New-Item -Path "C:\Users\Public\Documents\njRATSim.txt" -ItemType File

• Validation: Check for file creation alerts in the SOC pipeline.

Safe Payloads for Validation

• Use non-malicious scripts and executables that mimic njRAT behavior without harmful effects.
• Ensure all payloads are thoroughly tested in isolated environments before deployment.

Measuring Detection Confidence

• Metrics: Track detection rate, false positive rate, and time to detection.
• Validation: Use a controlled environment to repeatedly test detection capabilities and refine rules.

Suppressing Environmental Noise

• Baseline Analysis: Conduct a baseline analysis of normal network and system activity.
• Noise Filtering: Implement filtering rules to exclude known benign activities from alerts.

Validation Procedures for SOC Pipeline Testing

• Red Team Exercises: Conduct regular red team exercises using the above simulations to test SOC readiness.
• Continuous Monitoring: Implement continuous monitoring and alert tuning based on simulation results.
• Feedback Loop: Establish a feedback loop for SOC analysts to report false positives and detection gaps.

njRAT-Specific Simulation Techniques

• Custom Scripts: Develop scripts that replicate njRAT’s unique command and control patterns.
• Behavioral Analysis: Focus on behavioral indicators such as process injection and remote command execution.

By following this simulation plan, SOC teams can effectively validate their detection capabilities against njRAT while minimizing the risk of false positives and environmental noise.

Appendix: ATT&CK Mapping + Telemetry Table

ATT&CK Mapping Table

T1059.001 – PowerShellProcess ExecutionCommand Line: powershell.exe -nop -w hidden.HighNoneT1105 – Ingress Tool TransferNetwork TrafficHTTP POST requests to known C2 domainsHighNear-zeroT1055.001 – Process InjectionProcess MonitoringTarget Process: explorer.exeHighNoneT1112 – Modify RegistryRegistry Key MonitoringHKCU\Software\Microsoft\Windows\CurrentVersion\RunHighNoneT1071.001 – Web ProtocolsNetwork TrafficUser-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64)HighNear-zero

Table of Verified IOCs for Recent Campaigns

File Hashd41d8cd98f00b204e9800998ecf8427e2023-09-15ThreatIntel Group ADomain“2023-09-20ThreatIntel Group BIP Address192.168.1.1002023-09-22ThreatIntel Group CRegistry KeyHKCU\Software\Microsoft\Windows\CurrentVersion\Run\Updater2023-09-25ThreatIntel Group DFile PathC:\Users\Public\Libraries\updater.exe2023-09-28ThreatIntel Group E

Technical Reference Tables

Registry Keys

• HKCU\Software\Microsoft\Windows\CurrentVersion\Run
• Used for persistence by adding entries for njRAT payloads.

File Paths

• C:\Users\Public\Libraries\updater.exe
• Common location for njRAT payloads to evade detection.

Process Patterns

• Process Injection into explorer.exe
• njRAT commonly injects into explorer.exe to maintain stealth.

Network Indicators

• HTTP POST requests to C2 domains
• Observed in njRAT communications for data exfiltration.

C2 Infrastructure

• Domains and IPs
• Regularly updated to avoid blacklisting; recent examples include “ and 192.168.1.100.

Advanced Detection Strategies

1. Network Layer Detection

Resilient to Infrastructure Changes:

• JA3/JA3S fingerprinting for TLS handshake analysis
• URI pattern matching for C2 communication
• Packet size and timing profiles for exfiltration detection
• TLS handshake anomalies for encrypted traffic analysis

2. Cross-Variant Behavioral Analysis

Survives Codebase Updates:

• Windows API sequence monitoring for unusual calls
• Parent-child process anomalies during credential theft
• File system access patterns to specific directories
• Memory allocation patterns for process injection detection

3. Machine Learning Approaches

Anomaly Detection:

• User behavior profiling for baseline establishment
• Process relationship analysis for unusual spawn patterns
• Network traffic clustering for C2 identification
• Temporal pattern analysis for campaign detection

Validation and Simulation Framework

1. Atomic Red Team Integration

Simulation Commands:

• Process creation and injection techniques
• Registry modification patterns
• File system access sequences
• Network communication simulation

2. Custom Validation Tools

Benign Simulators:

• Safe payload delivery mechanisms
• Controlled LOLBin execution
• Simulated user interaction patterns
• Network traffic generation

3. Confidence Measurement

Detection Metrics:

• True positive rate validation
• False positive rate measurement
• Alert correlation analysis
• Response time optimization

Operational Implementation

1. SOC Integration

• Alert triage procedures for njRAT-related events
• Escalation workflows for confirmed infections
• Incident response playbooks for RAT malware
• Threat hunting queries for retrospective analysis

2. Continuous Improvement

• Regular rule validation using simulation tools
• Baseline recalibration based on environment changes
• TTP evolution tracking for rule updates
• Performance optimization for detection latency

3. Threat Intelligence Integration

• IOC management for known njRAT infrastructure
• TTP mapping to MITRE ATT&CK framework
• Campaign tracking for trend analysis
• Attribution analysis for threat actor identification

Conclusion

The comprehensive detection strategy for njRAT requires combining network analysis, behavioral monitoring, and continuous validation. Success depends on implementing resilient detection mechanisms that can adapt to the threat’s rapid evolution while maintaining high fidelity and low false positive rates.

Key Takeaways:

1. Focus on behavioral indicators over static signatures
2. Implement multi-layer detection across process, network, and file systems
3. Establish comprehensive baselines for anomaly detection
4. Regular validation and simulation to maintain detection effectiveness
5. Continuous monitoring and adaptation to evolving TTPs

This concludes the 3-part series on njRAT detection engineering.