IoT Network Anomaly Detection with Session, Volumetric, and APN Analysis
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Solution Overview
Problem
The rapid expansion of telecommunication networks supporting connected vehicles and IoT devices has led to increased data generation and network traffic, necessitating efficient and accurate methods for detecting anomalies to maintain network reliability, prevent fraudulent activities, and ensure accurate billing.
Innovation Solution
A system and method for anomaly detection in telecommunication networks that analyze call detail record (CDR) data to identify session, volumetric, and APN anomalies, generating reports for users to take corrective action, and initiating modifications to prevent subsequent anomalies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional anomaly detection methods are used in telecommunication networks, then the system complexity is low, but the detection accuracy and reliability are insufficient to handle the unprecedented increase in data generation and network traffic from IoT devices and connected vehicles
Solution Approach 1:
The patent segments anomaly detection into three distinct analysis dimensions: session-based anomaly detection (analyzing communication session patterns), volumetric anomaly detection (analyzing data volume metrics), and APN-based anomaly detection (analyzing access point name usage patterns). This segmentation allows each detection type to be processed independently with specialized algorithms, improving overall detection accuracy while managing system complexity through modular architecture.
Solution Approach 2:
The patent introduces multiple analysis dimensions beyond traditional single-metric detection. By simultaneously analyzing session characteristics, volumetric metrics, and APN patterns across multiple time scales and device types, the system transforms the detection problem from a single-dimensional approach to a multi-dimensional analysis framework, significantly enhancing anomaly detection capability.
2Measurement precision
If comprehensive data analysis is performed on all network traffic, then anomaly detection accuracy improves, but the processing time and computational resources increase significantly
Solution Approach 1:
The patent implements partial action by selectively applying different analysis depths to different data types. Session-based analysis focuses on key communication patterns, volumetric analysis concentrates on critical threshold metrics, and APN-based analysis targets specific access patterns. This selective approach achieves sufficient detection accuracy without processing every single data point in exhaustive detail, thereby reducing processing time while maintaining effective anomaly identification.
3Reliability
If real-time anomaly detection is implemented to prevent fraudulent activities, then network reliability improves, but the computational load and energy consumption increase
Solution Approach 1:
The patent implements periodic action through scheduled batch processing of anomaly detection at strategic intervals rather than continuous real-time analysis. The system periodically analyzes session data, volumetric metrics, and APN patterns at defined checkpoints in the data flow, maintaining network reliability through regular monitoring while avoiding the sustained high computational load of continuous real-time processing, thus reducing overall energy consumption.
Data Source
AI summary
Aspects of the subject disclosure may include, for example, retrieving call detail record (CDR) data for a plurality of devices, each device of the plurality of devices using a subscriber identity module (SIM) to access a mobility network, identifying data anomalies for the plurality of devices, wherein the identifying the data anomalies is based on the CDR data, wherein the data anomalies may be indicative of inappropriate usage of the mobility network, identifying a device associated with a data anomaly, and initiating a modification of the device to prevent subsequent anomalies. Other embodiments are disclosed.