Google llc (20240127055). NETWORK ANOMALY DETECTION simplified abstract

NETWORK ANOMALY DETECTION

Organization Name

google llc

Inventor(s)

James Peroulas of San Mateo CA (US)

Poojita Thukral of Mountain View CA (US)

Dutt Kalapatapu of Santa Clara CA (US)

Andreas Terzis of Mountain View CA (US)

Krishna Sayana of Mountain View CA (US)

NETWORK ANOMALY DETECTION - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240127055 titled 'NETWORK ANOMALY DETECTION

Simplified Explanation

The method described in the abstract is for detecting network anomalies by analyzing control messages from a cellular network using a predictive model. The model predicts potential labels for the messages based on extracted features and determines if the message corresponds to a network performance issue. If an issue is detected, it is communicated to the responsible network entity.

• The method involves receiving a control message from a cellular network.
• Extracting features from the control message.
• Predicting a potential label for the message using a predictive model trained on a set of training control messages.
• Determining if the probability of the potential label satisfies a confidence threshold.
• Analyzing the message to identify network performance issues.
• Communicating detected network performance issues to the responsible network entity.

Potential Applications

This technology can be applied in telecommunications companies to improve network performance and reliability by detecting and addressing anomalies in real-time.

Problems Solved

This technology helps in proactively identifying and resolving network performance issues before they impact user experience, leading to better service quality and customer satisfaction.

Benefits

The benefits of this technology include enhanced network performance, reduced downtime, improved customer experience, and increased operational efficiency for network operators.

Potential Commercial Applications

A potential commercial application of this technology is in the telecommunications industry for network monitoring and management solutions, offering proactive anomaly detection and performance optimization services.

Possible Prior Art

One possible prior art for this technology could be existing network monitoring systems that use rule-based approaches to detect anomalies but may not be as effective or efficient as predictive modeling methods.

How does the predictive model handle new or unseen types of network anomalies that were not present in the training data?

The predictive model may need to be continuously updated and retrained with new data to adapt to emerging network anomalies and ensure accurate detection.

What are the potential privacy implications of analyzing control messages for network anomaly detection?

Analyzing control messages for anomaly detection may raise privacy concerns regarding the monitoring and analysis of network data. Network operators must ensure compliance with data protection regulations and user privacy rights.

Original Abstract Submitted

a method for detecting network anomalies includes receiving a control message from a cellular network and extracting one or more features from the control message. the method also includes predicting a potential label for the control message using a predictive model configured to receive the one or more extracted features from the control message as feature inputs. here, the predictive model is trained on a set of training control messages where each training control message includes one or more corresponding features and an actual label. the method further includes determining that a probability of the potential label satisfies a confidence threshold. the method also includes analyzing the control message to determine whether the control message corresponds to a respective network performance issue. when the control message impacts network performance, the method includes communicating the network performance issue to a network entity responsible for the network performance issue.