Nvidia corporation (20240106848). VISUALIZATION TECHNOLOGY FOR FINDING ANOMALOUS PATTERNS simplified abstract
Contents
- 1 VISUALIZATION TECHNOLOGY FOR FINDING ANOMALOUS PATTERNS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 VISUALIZATION TECHNOLOGY FOR FINDING ANOMALOUS PATTERNS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
VISUALIZATION TECHNOLOGY FOR FINDING ANOMALOUS PATTERNS
Organization Name
Inventor(s)
Ajay Anil Thorve of Maple Valley WA (US)
Allan Enemark of Santa Cruz CA (US)
Rachel Allen of Arlington VA (US)
Bartley Richardson of Alexandria VA (US)
VISUALIZATION TECHNOLOGY FOR FINDING ANOMALOUS PATTERNS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240106848 titled 'VISUALIZATION TECHNOLOGY FOR FINDING ANOMALOUS PATTERNS
Simplified Explanation
The patent application describes technologies for generating a graphical user interface (GUI) dashboard with a three-dimensional (3D) grid of unit cells. An anomaly statistic can be determined for a set of records, and a subset of network address identifiers can be identified and sorted according to the anomaly statistic. The GUI dashboard is organized with unit cells representing network address identifiers as rows, time intervals as columns, colors as anomaly score indicators, and network access events as column heights.
- Subset of network address identifiers sorted by anomaly statistic
- GUI dashboard with 3D grid of unit cells representing anomaly scores and network access events
Potential Applications
This technology can be applied in cybersecurity for monitoring and detecting anomalies in network traffic. It can also be used in data analysis for visualizing patterns and trends in large datasets.
Problems Solved
This technology helps in quickly identifying network address identifiers with higher anomaly statistics, allowing for proactive measures to be taken against potential security threats. It also simplifies the visualization of complex data sets, making it easier for users to interpret and analyze the information.
Benefits
The GUI dashboard provides a visually intuitive way to monitor network activity and identify potential security breaches. It streamlines the process of analyzing large amounts of data by presenting it in a structured and organized manner.
Potential Commercial Applications
This technology can be valuable for cybersecurity companies, data analysis firms, and organizations looking to enhance their network monitoring capabilities. A potential SEO-optimized title for this section could be "Commercial Applications of 3D GUI Dashboard for Network Anomaly Detection."
Possible Prior Art
One possible prior art could be existing network monitoring tools that provide visual representations of network traffic and anomalies. These tools may not have the specific features and organization described in this patent application.
Unanswered Questions
How does the 3D visualization of the GUI dashboard enhance anomaly detection compared to traditional 2D interfaces?
The 3D visualization allows for a more immersive and interactive experience, potentially making it easier for users to spot patterns and anomalies in the data. It can provide a more comprehensive view of the network activity, leading to more effective anomaly detection.
What are the potential limitations or challenges in implementing this technology in real-world network monitoring systems?
Some potential challenges could include the processing power required to render the 3D GUI dashboard in real-time, as well as the scalability of the system to handle large volumes of network traffic data. Additionally, user training and adoption of the new interface may also be a factor to consider.
Original Abstract Submitted
technologies for generating a graphical user interface (gui) dashboard with a three-dimensional (3d) grid of unit cells are described. an anomaly statistic can be determined for a set of records. a subset of network address identifiers can be identified and sorted according to the anomaly statistic. the subset can have higher anomaly statistics than other network address identifiers. there can be a maximum number in the subset. the gui dashboard is generated with unit cells organized by the subset of network address identifiers as rows, time intervals as columns, colors as a configurable anomaly score indicator, and a number of network access events as column heights. each unit cell is a colored, 3d visual object representing a composite score of anomaly scores associated with zero or more network access events corresponding to the respective network address identifier at the respective time interval. the gui dashboard is rendered on a display.
