5G Signaling Storm Detection via Dual-Plane ML Quarantine
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Solution Overview
Problem
Current security measures for IoT devices are inadequate in detecting and mitigating abnormal network behavior and DDoS attacks in 5G networks, as conventional protection methods are ineffective in managing the high density of IoT devices and the unique characteristics of 5G networks.
Innovation Solution
A system that includes a Protector Unit, UP Probe, and CP Probe to monitor and analyze control messages in both the User Plane and Control Plane, using Machine Learning to detect abnormal communication patterns and trigger quarantine measures for compromised IoT devices, while leveraging IoT device profiles to enforce security policies and mitigate signaling storms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional security protection methods are used for IoT devices in 5G networks, then device security is maintained, but the system cannot effectively detect and mitigate DDoS attacks and abnormal network behavior
Solution Approach 1:
The system dynamically adapts security monitoring by establishing baseline behavior profiles for IoT devices and dynamically detecting deviations from these baselines. The anomaly detection mechanism continuously learns normal device behavior patterns and adapts to changing network conditions, enabling effective detection of DDoS attacks and abnormal behaviors that static conventional security measures cannot detect.
Solution Approach 2:
The patent replaces conventional mechanical security measures with machine learning-based anomaly detection systems. Instead of relying on predefined security rules and signatures, the system uses AI algorithms to learn normal device behavior patterns and automatically detect anomalies, substituting traditional security mechanisms with intelligent, adaptive detection capabilities.
2Measurement precision
If monitoring of all IoT devices is implemented to detect abnormal behavior, then detection accuracy improves, but network overhead and processing complexity increase
Solution Approach 1:
The system segments the monitoring task by dividing IoT devices into groups based on their baseline behavior profiles and communication patterns. Instead of uniformly monitoring all devices with the same level of intensity, the system segments monitoring resources according to device types, behavior categories, and anomaly risk levels, reducing overall system complexity while maintaining detection accuracy.
Solution Approach 2:
The system applies different monitoring strategies and detection thresholds to different device groups based on their specific characteristics. Each device or device group receives customized monitoring parameters and anomaly detection criteria tailored to its normal behavior patterns, enabling precise detection without requiring uniform complex monitoring across all devices.
3Reliability
If quarantine measures are taken for suspected compromised devices, then network protection improves, but false positives may disrupt legitimate device operations
Solution Approach 1:
The system performs preliminary analysis by continuously establishing baseline behavior profiles for all IoT devices before anomalies occur. By pre-learning normal device patterns and communication behaviors, the system can more accurately distinguish between legitimate device operations and actual threats, reducing false positives in quarantine decisions and maintaining operational continuity for legitimate devices.
Solution Approach 2:
The system implements feedback mechanisms where quarantine decisions are continuously evaluated and adjusted based on device responses and additional analysis. When devices are quarantined, the system monitors their behavior patterns and provides feedback to refine anomaly detection algorithms, reducing false positives over time while maintaining effective protection against real threats.
Data Source
AI summary
Detecting, mitigating and isolating a Signaling Storm, particularly in 5G communication networks. A Control Plane signal probe is connected at a first network node located between a Radio Access Network and a 5G Core Network, to monitor control messages originating from 5G-capable devices. A User Plane signal probe is connected at a second network node located between the 5G Core Network and remote entities to which the 5G-capable devices are sending messages, to monitor control messages passing through the second network node. An Inventory Management sub-system stores data correlating between 5G-capable devices and IMSI numbers. A Protector Unit is configured to receive (i) data collected by the Control Plane signal probe, and (ii) data collected by the User Plane signal probe, and (iii) a subset of IMSI numbers. The Protector Unit performs Machine Learning analysis, and detects and quarantines particular 5G-capable devices that are compromised or malfunctioning.