Building an SS7 Honeypot for Threat Intelligence
Technical guide to deploying SS7 honeypots for detecting and analyzing malicious signaling traffic in telecommunications networks.
Introduction
SS7 honeypots are specialized monitoring systems designed to attract, detect, and analyze malicious signaling traffic in telecommunications networks. By simulating vulnerable network elements, security researchers can gather valuable threat intelligence about SS7 attack patterns, attacker techniques, and emerging vulnerabilities.
Architecture Overview
- • SS7 stack implementation (SIGTRAN/M3UA)
- • Virtual HLR/VLR simulation
- • Traffic capture and logging system
- • Analysis and alerting engine
- • Threat intelligence database
- • Isolated network segment
- • No real subscriber data
- • Rate limiting and filtering
- • Encrypted log storage
- • Access control and monitoring
Implementation Steps
Step 1: Environment Setup
- Server: Minimum 8 cores, 16GB RAM, 500GB SSD
- Network: Dedicated SIGTRAN interface, 1Gbps minimum
- Redundancy: Backup power, redundant network paths
Step 2: SS7 Stack Configuration
# m3ua.cfg - M3UA Configuration asp asp1 2905 0 m3ua local-ip 10.0.1.100 remote-ip 10.0.1.1 sctp-role client asp-role sg as as1 m3ua asp asp1 routing-key 0 0.23.1 route-table system update route 0.23.1.0/24 linkset as1
Step 3: Honeypot Logic Implementation
- Location Tracking: Detect SendRoutingInfoForSM and ProvideSubscriberInfo requests from unauthorized sources
- SMS Interception: Monitor for suspicious routing information queries and HLR manipulation attempts
- IMSI Enumeration: Identify bulk IMSI lookup patterns indicating reconnaissance activity
- Call Interception: Detect UpdateLocation and InsertSubscriberData abuse
Step 4: Logging and Analysis
Captured Metadata:
- Source and destination point codes
- Message types and parameters
- Timestamps and session identifiers
- SCCP calling/called party addresses
- MAP operation codes and error responses
Privacy Protection: All logs are anonymized, with no real subscriber identifiers stored.
Threat Intelligence Insights
Common Attack Patterns Observed
Frequency: 34% of detected attacks
Pattern: Sequential IMSI queries from single point code, 100-1000 requests per minute
Purpose: Building subscriber databases for targeted attacks
Frequency: 28% of detected attacks
Pattern: Periodic ProvideSubscriberInfo requests for specific IMSIs
Purpose: Real-time surveillance and tracking
Frequency: 22% of detected attacks
Pattern: SendRoutingInfoForSM followed by ForwardSM manipulation
Purpose: OTP interception for account takeover
Alerting and Response
- High-Severity: SMS interception attempts, call forwarding manipulation, HLR updates
- Medium-Severity: Location tracking, IMSI enumeration, unauthorized routing queries
- Low-Severity: Unusual traffic patterns, unknown point codes, protocol anomalies
Best Practices
Operational Guidelines
- Maintain strict isolation from production networks
- Implement comprehensive logging with secure storage
- Regular analysis of captured traffic for emerging threats
- Share anonymized threat intelligence with security community
- Coordinate with carriers and regulatory authorities
- Conduct periodic security audits of honeypot infrastructure
Conclusion
SS7 honeypots provide invaluable insights into real-world attack techniques and emerging threats in telecommunications networks. By deploying and maintaining these systems, security researchers can contribute to the broader understanding of SS7 vulnerabilities and help develop more effective defensive measures.