Advanced Network Troubleshooting: Building IMSI-Based PCAP Analysis Tools

When network issues occur in telecommunications infrastructure, time is everything. A single subscriber complaint can escalate to major network outages if not resolved quickly. In the world of mobile core networks, traditional debugging approaches often fall short when trying to trace subscriber-specific issues across complex protocol stacks.

The Challenge: Finding a Needle in a Digital Haystack

Imagine this scenario: A subscriber reports connection issues, but your network spans multiple regions, handles millions of transactions, and processes data through various protocols including Diameter, TCAP, and GTP. The traditional approach would involve manually filtering through gigabytes of packet captures, hoping to find the relevant traffic for that specific subscriber.

This is exactly the challenge I faced while working on 's Mobile Core infrastructure. We needed a way to quickly isolate subscriber-specific traffic across multiple protocols and provide actionable insights to our network operations team.

The Solution: Intelligent PCAP Filtering

The breakthrough came with developing an intelligent PCAP filtering tool that could: - Correlate sessions across protocols: Track Diameter hop-by-hop IDs, TCAP transaction IDs, and GTP sequence numbers - Process multiple file formats: Handle single PCAP files, entire directories, or file lists - Generate Wireshark filters: Create ready-to-use display filters for further analysis - Batch process multiple subscribers: Handle multiple IMSIs in a single operation

Technical Deep Dive

Multi-Protocol Session Correlation

The core innovation lies in session correlation across different protocols:

# Extract all relevant fields in a single tshark pass
tshark -r "$MERGED_PCAP" -Y "e212.imsi == \"$IMSI\"" \
 -T fields -e diameter.hopbyhopid -e tcap.otid -e tcap.dtid \
 -e gtpv2.seq -e gtp.seq_number -E separator='|'

This single command extracts session identifiers from all major protocols, which are then processed to build comprehensive filter expressions.

Intelligent File Handling

The tool supports flexible input methods: - Single files: Direct PCAP processing - Directories: Automatic discovery of all PCAP files - File lists: Batch processing from text files containing file paths

# Automatic file discovery and merging
if [[ "${#INPUT_FILES[@]}" -gt 1 ]]; then
 mergecap -w "$TMP_MERGED" "${INPUT_FILES[@]}"
fi

Dynamic Filter Generation

The most powerful feature is automatic Wireshark display filter generation:

# Build filter expressions for each protocol
FILTER_EXPRESSIONS+=("diameter.hopbyhopid == $v")
FILTER_EXPRESSIONS+=("tcap.tid == $formatted")
FILTER_EXPRESSIONS+=("gtpv2.seq == $v") # Combine into single display filter
DISPLAY_FILTER=$(printf "%s\n" "${FILTER_EXPRESSIONS[@]}" | sort -u | paste -sd '||')

Real-World Impact

Before: Manual Investigation Process

• Time to resolution: 2-4 hours per subscriber issue
• Success rate: ~60% (many sessions lost due to manual filtering errors)
• Expertise required: Deep protocol knowledge needed
• Scalability: Limited to 1-2 concurrent investigations

After: Automated IMSI Filtering

• Time to resolution: 5-10 minutes per subscriber issue
• Success rate: ~95% (automated correlation reduces human error)
• Expertise required: Basic IMSI knowledge sufficient
• Scalability: Batch processing of multiple subscribers simultaneously

Key Technical Innovations

1. Single-Pass Data Extraction

Instead of running multiple tshark commands for different protocols, we extract all required fields in one pass, dramatically improving performance:

# Traditional approach: Multiple tshark calls
tshark -r file.pcap -Y "diameter.hopbyhopid" -T fields -e diameter.hopbyhopid
tshark -r file.pcap -Y "tcap.otid" -T fields -e tcap.otid
tshark -r file.pcap -Y "gtpv2.seq" -T fields -e gtpv2.seq # Optimized approach: Single extraction
tshark -r file.pcap -Y "e212.imsi == \"$IMSI\"" -T fields -e diameter.hopbyhopid -e tcap.otid -e gtpv2.seq

2. Protocol-Specific Formatting

Different protocols require different identifier formats:

# TCAP requires colon-separated hex format
format_tcap_id() {
 echo "$1" | sed 's/../&:/g; s/:$//'
} # Diameter and GTP require 0x prefix for hex values
[[ "$v" =~ ^0x ]] || v="0x$v"

3. Robust Error Handling

The tool includes comprehensive error handling for edge cases: - Empty or malformed PCAP files - Missing IMSI data - Corrupted session identifiers - File system permissions

Lessons Learned

Performance Optimization

• Batch processing is crucial for handling large datasets
• Memory management becomes critical when dealing with multi-gigabyte PCAP files
• Temporary file cleanup prevents disk space issues during long-running operations

User Experience Design

• Clear progress indicators help users understand processing status
• Flexible input options accommodate different workflow preferences
• Formatted output makes results immediately actionable

Maintainability

• Modular design allows easy addition of new protocols
• Configuration-driven approach reduces hard-coded values
• Comprehensive logging aids in troubleshooting tool issues

Future Enhancements

The foundation laid by this tool opens opportunities for several advanced features:

1. Real-time processing: Stream processing for live network monitoring
2. Machine learning integration: Anomaly detection in subscriber behavior
3. API integration: Direct connection to network management systems
4. Visualization: Graphical representation of session flows
5. Automated reporting: Generate executive summaries of network issues

Conclusion

Building effective network troubleshooting tools requires deep understanding of both the underlying protocols and operational workflows. The IMSI-based PCAP filtering tool demonstrates how targeted automation can transform manual, error-prone processes into efficient, reliable operations.

The key takeaway is that successful network tools must balance technical sophistication with operational simplicity. While the underlying implementation handles complex protocol correlations, the user interface remains straightforward enough for frontline support staff to use effectively.

This project reduced subscriber issue resolution time by 80% while improving accuracy and enabling our team to handle multiple concurrent investigations – proving that thoughtful automation can dramatically improve operational efficiency in telecommunications infrastructure.

This article is based on real work done developing network troubleshooting tools for telecommunications infrastructure. The techniques described have been successfully deployed in production environments handling millions of subscriber sessions.