18194213. PREDICTIVE MACHINE LEARNING ARCHITECTURE FOR IDENTIFYING GAPS IN NETWORK ACTIVITY simplified abstract (Visa International Service Association)

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PREDICTIVE MACHINE LEARNING ARCHITECTURE FOR IDENTIFYING GAPS IN NETWORK ACTIVITY

Organization Name

Visa International Service Association

Inventor(s)

Tomas Cacicedo of Coral Gables FL (US)

Arya Eskamani of Miami FL (US)

Debesh Kumar of Foster City CA (US)

PREDICTIVE MACHINE LEARNING ARCHITECTURE FOR IDENTIFYING GAPS IN NETWORK ACTIVITY - A simplified explanation of the abstract

This abstract first appeared for US patent application 18194213 titled 'PREDICTIVE MACHINE LEARNING ARCHITECTURE FOR IDENTIFYING GAPS IN NETWORK ACTIVITY

    • Simplified Explanation:**

The patent application describes systems and methods for classifying gaps in network activity as normal or anomalous. A computer system can identify time gaps between network events, use data features to train a machine learning model, and classify unlabeled time gaps based on those features.

    • Key Features and Innovation:**
  • Computer system identifies time gaps between network events
  • Uses data features from network event data records to train a machine learning model
  • Classifies unlabeled time gaps as normal or anomalous based on trained model
    • Potential Applications:**

This technology can be applied in network security systems to detect abnormal network activity, in monitoring systems to identify potential issues or threats, and in optimizing network performance by analyzing gaps in activity.

    • Problems Solved:**

The technology addresses the challenge of efficiently classifying gaps in network activity as normal or anomalous, helping to improve network security, monitoring, and performance.

    • Benefits:**
  • Enhanced network security by detecting anomalous activity
  • Improved monitoring capabilities to identify potential threats
  • Optimized network performance through analysis of gaps in activity
    • Commercial Applications:**

Title: Network Activity Classification System This technology can be utilized by cybersecurity companies, network monitoring firms, and IT departments to enhance network security, detect threats, and optimize network performance. The market implications include increased efficiency, improved security, and better network management.

    • Prior Art:**

Readers can explore prior art related to network activity classification, machine learning in network security, and gap analysis in network monitoring to gain a deeper understanding of the technology landscape.

    • Frequently Updated Research:**

Researchers are continually exploring advancements in machine learning algorithms for network security, anomaly detection in network activity, and real-time monitoring solutions for network performance.

    • Questions about Network Activity Classification:**

1. How does this technology contribute to improving network security? 

  - This technology enhances network security by efficiently classifying gaps in network activity as normal or anomalous, helping to detect potential threats.

2. What are the potential commercial applications of this network activity classification system? 

  - The commercial applications include enhancing cybersecurity measures, improving network monitoring capabilities, and optimizing network performance for various industries.


Original Abstract Submitted

Systems and methods for classifying gaps in network activity as normal or anomalous are disclosed. A computer system can identify time gaps between successive network events, which can comprise communications or interactions between entities or devices on a network. The computer system can identify network event data records corresponding to network events that occurred both before and after the identified time gaps. The computer system can use data contained in network event data records corresponding to these network events to derive data features that can be used to train a machine learning to classify time gaps based on those features. After training the machine learning model, the computer system can then extract data features corresponding to unlabeled time gaps, and input those data features into the trained machine learning model in order to classify those time gaps as normal or anomalous.

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