Cell Signaling Clustering for Targeted Network Anomaly Detection
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Solution Overview
Problem
Existing communication networks face challenges in detecting and mitigating distributed denial of service (DDoS) attacks and signaling storms originating from within the network, as traditional methods are inadequate and often result in indiscriminate blocking, lacking dedicated anomaly detection for each cell, and are unable to handle the varying signaling characteristics of different cells effectively.
Innovation Solution
A security management component employs artificial intelligence and machine learning techniques to cluster cells based on their signaling behavior, using neural networks to create normal behavior profiles and detect abnormal conditions, enabling accurate anomaly detection and alerting mechanisms to facilitate targeted mitigation actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional DDoS detection methods are used, then network security is maintained, but false positives increase and critical communications are interrupted
Solution Approach 1:
The patent segments the network into multiple cell clusters, each with its own anomaly detection profile. Instead of applying a uniform detection threshold across the entire network, the system divides it into manageable segments (clusters) that can be analyzed independently. This segmentation allows for more precise localised detection without affecting other parts of the network, thereby reducing false positives while maintaining security.
Solution Approach 2:
The system implements local quality by creating cell-specific normal behavior profiles that capture the unique signaling characteristics of each cell or cluster. Each cell receives tailored detection parameters based on its historical behavior patterns, rather than applying a one-size-fits-all approach. This localised adaptation enables accurate anomaly detection specific to each cell's characteristics, reducing false alarms while maintaining reliable security.
2Reliability
If indiscriminate blocking is applied to mitigate DDoS attacks, then network security is improved, but network efficiency deteriorates due to interruption of legitimate communications
Solution Approach 1:
The system employs dynamic anomaly detection that continuously adapts to changing network conditions and cell-specific behaviors. The normal behavior profiles are updated over time to reflect evolving patterns, allowing the system to distinguish between legitimate changes in traffic patterns and actual anomalies. This dynamic approach prevents unnecessary blocking of legitimate communications while maintaining security against evolving threats.
Solution Approach 2:
The system implements feedback mechanisms where detection results and mitigation actions are continuously monitored and used to refine future detection decisions. When potential anomalies are detected, the system evaluates them against established profiles and only triggers blocking actions when confidence thresholds are met. This feedback loop ensures that legitimate communications are not unnecessarily interrupted while maintaining effective security response.
3Measurement precision
If dedicated anomaly detection is implemented for each cell, then detection precision is improved, but device complexity increases
Solution Approach 1:
The patent merges cells into clusters based on similar signaling characteristics and geographic proximity, reducing the number of individual profiles that need to be maintained. Instead of creating completely separate detection systems for each cell, the system groups cells with similar behaviors into clusters that share common profiles. This merging approach maintains detection precision for individual cells while significantly reducing the overall complexity of the detection system.
Solution Approach 2:
The system implements universal detection mechanisms that can be applied across multiple cells through clustering. The anomaly detection framework, profile creation processes, and threshold evaluation methods are designed to be reusable across different cell groups. This multi-functional approach allows the system to maintain dedicated detection capabilities for each cell while using shared infrastructure and algorithms, thereby reducing overall system complexity.
Data Source
AI summary
Cells of a network can be clustered based on signaling behavior, and abnormal signaling conditions against cells can be detected and mitigated. Signal measurement data from a group of cell devices in a cellular network can be applied to a neural network. The neural network can be selected to detect abnormal conditions in the cellular network based on reduced dimensionality encoding and decoding of prior signal measurement data from the cellular network. The neural network can generate encoded reduced dimensionality vectors from the signal measurement data. A cluster of cell devices from the group of cell devices can be generated based on a relative proximity of the encoded reduced dimensionality vectors. The neural network determines potential abnormal conditions in the cluster of cell devices based on an application of new signal measurement data from the cluster of cell devices.