SS7 and SIP Signaling Threat Detection

Monitors telecom signaling traffic to detect SS7 and SIP attacks that evade traditional billing-record-based security monitoring.

The Problem

“SS7 and SIP signaling-layer threat detection for telecom networks”

Organizations face these key challenges:

1

Trust-based SS7 architecture allows abuse by connected partners and intermediaries

2

SIP exposure on IP networks increases attack surface and event volume

3

Billing-record monitoring lacks message-level context and is too delayed for prevention

4

Static rules struggle with evolving attack patterns and partner-specific behavior

Impact When Solved

Detects SS7 and SIP attacks before they appear in billing recordsImproves visibility into protocol misuse, spoofing, scanning, and anomalous routingReduces false positives by combining protocol rules with behavioral baselinesPrioritizes incidents by subscriber, partner, and network-element risk

The Shift

Before AI~85% Manual

Human Does

  • •Review CDR/FDR reports and signaling traces after incidents
  • •Maintain static detection rules and thresholds for known abuse patterns
  • •Investigate partner, trunk, and subscriber anomalies manually
  • •Prioritize escalations based on customer impact, fraud exposure, and service disruption

Automation

  • •Parse signaling and billing logs into searchable records
  • •Apply fixed protocol signatures and threshold-based alerts
  • •Aggregate event counts by source, destination, and message type
  • •Generate basic alert queues and historical reports
With AI~75% Automated

Human Does

  • •Approve containment actions for high-risk peers, routes, or subscriber segments
  • •Decide incident severity, partner escalation, and response priorities
  • •Handle ambiguous cases, false-positive disputes, and policy exceptions

AI Handles

  • •Monitor SS7 and SIP signaling in near real time for protocol misuse and attack patterns
  • •Learn behavioral baselines by subscriber, partner, route, trunk, and network element
  • •Correlate multi-stage events into prioritized incidents with entity risk scoring
  • •Recommend and optionally trigger approved response actions such as rate limits or blocking

Operating Intelligence

How it works

AI watches every signal continuously.

Humans investigate what it flags.

False positives train the next watch cycle.

Confidence88%
ArchetypeMonitor & Flag
Shape6-step linear
Human gates1
Autonomy
67%AI controls 4 of 6 steps

Who is in control at each step

Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.

Loop shapelinear

Step 1

Observe

Step 2

Classify

Step 3

Route

Step 4

Exception Review

Step 5

Record

Step 6

Feedback

AI lead

Autonomous execution

1AI
2AI
3AI
5AI
gate

Human lead

Approval, override, feedback

4Human
6↺ Loop
AI-led step
Human-controlled step
Feedback loop
TL;DR

AI observes and classifies continuously. Humans only engage on flagged exceptions. Corrections sharpen future detection.

The Loop

6 steps

1 operating angles mapped

Operational Depth

Technologies

Technologies commonly used in SS7 and SIP Signaling Threat Detection implementations:

Protocol rules libraryOther
4 mentions
Real-time alerting pipelineOther
4 mentions
Threat detection dashboardOther
4 mentions
SIP traffic inspectionOther
3 mentions
SS7 security monitoringOther
3 mentions

Key Players

Companies actively working on SS7 and SIP Signaling Threat Detection solutions:

Subex

Real-World Use Cases

Signaling-layer security monitoring for SS7 and SIP attack detection

The operator inspects the control messages that tell the network how to route calls and texts, so it can spot malicious commands or suspicious behavior before customers are harmed.

Protocol-aware threat detection and behavioral deviation analysispractical and increasingly necessary as networks modernize, though not yet universal across operators.
10.0

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