View source for 18060753. DETECTING AN ANOMALY EVENT IN LOW DIMENSIONAL SPACENETWORKS simplified abstract (HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP)

=DETECTING AN ANOMALY EVENT IN LOW DIMENSIONAL SPACENETWORKS=


==Organization Name==

[[:Category:HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP|HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP]]


[[Category:HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP]]

==Inventor(s)==

[[:Category:Biswadeb Dutta of Chestnut Hill MA (US)|Biswadeb Dutta of Chestnut Hill MA (US)]][[Category:Biswadeb Dutta of Chestnut Hill MA (US)]]

[[:Category:Didier Albet of Mougins (FR)|Didier Albet of Mougins (FR)]][[Category:Didier Albet of Mougins (FR)]]

[[:Category:Jean-Charles Picard of Mougins (FR)|Jean-Charles Picard of Mougins (FR)]][[Category:Jean-Charles Picard of Mougins (FR)]]

==DETECTING AN ANOMALY EVENT IN LOW DIMENSIONAL SPACENETWORKS - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18060753 titled 'DETECTING AN ANOMALY EVENT IN LOW DIMENSIONAL SPACENETWORKS

==Simplified Explanation==

The patent application describes systems and methods for reducing a number of performance metrics generated by network functions to a number of reduced dimension metrics, which can be used to detect anomalous behavior and generate a warning signal of the detected anomalous behavior. The disclosed systems and methods transform raw performance metrics in a high dimensionality space to a reduced number of metrics in a lower dimensionality space through dimensionality reduction techniques. Anomalous behavior in network performance is detected in the high dimensionality space using the reduced dimension metrics. The systems and methods convert the reduced dimension metrics back to the high dimensionality space, allowing the performance metrics from network functions to be utilized to understand and address potential problems in the network.

* Systems and methods for reducing performance metrics to detect anomalous behavior in network functions
* Transformation of raw performance metrics to reduced dimension metrics through dimensionality reduction techniques
* Detection of anomalous behavior in network performance using reduced dimension metrics
* Conversion of reduced dimension metrics back to high dimensionality space for analysis and problem-solving in the network

== Potential Applications ==
The technology described in the patent application could be applied in various industries and sectors where network performance monitoring and anomaly detection are crucial, such as telecommunications, cybersecurity, and data centers.

== Problems Solved ==
1. Detection of anomalous behavior in network performance metrics
2. Reduction of high dimensionality data to more manageable and actionable metrics for analysis and problem-solving

== Benefits ==
1. Early detection of potential network issues or security threats
2. Improved network performance monitoring and management
3. Enhanced ability to address and resolve network problems efficiently

== Potential Commercial Applications ==
Optimizing network performance in telecommunications companies
Enhancing cybersecurity measures in data centers
Improving data transmission efficiency in large-scale networks

== Possible Prior Art ==
One possible prior art in this field is the use of machine learning algorithms for anomaly detection in network performance metrics. These algorithms analyze large volumes of data to identify patterns and deviations that may indicate anomalous behavior in the network.


==Original Abstract Submitted==

Systems and methods are provided for reducing a number of performance metrics generated by network functions to a number of reduced dimension metrics, which can be used to detect anomalous behavior and generate a warning signal of the detected anomalous behavior. The disclosed systems and methods transform raw performance metrics in a high dimensionality space to a reduced number of metrics in a lower dimensionality space through dimensionality reduction techniques. Anomalous behavior in network performance is detected in the high dimensionality space using the reduced dimension metrics. The systems and methods disclosed herein convert the reduced dimension metrics back to the high dimensionality space, such that the performance metrics from network functions can be utilized to understand and address potential problems in the network.



[[Category:H04L9/40]]