17957848. MACHINE LEARNING BASED SYSTEM(S) FOR NETWORK TRAFFIC DISCOVERY AND ANALYSIS simplified abstract (Bank of America Corporation)

MACHINE LEARNING BASED SYSTEM(S) FOR NETWORK TRAFFIC DISCOVERY AND ANALYSIS

Organization Name

Bank of America Corporation

Inventor(s)

Amer Ali of Jersey City NJ (US)

Brian Daniel Christman of Dublin TX (US)

Kamal D. Sharma of Mason OH (US)

Gilbert Gatchalian of Union NJ (US)

Karthik Rajagopalan of Glen Allen VA (US)

Kevin A. Delson of Woodland Hills CA (US)

Robert Ronald Rosseland, Jr. of Huntersville NC (US)

Yassine Touahri of Charlotte NC (US)

Jyothishwar Reddy Sama of Fort Mill SC (US)

Zaheeruddin Mohammed of Hyderabad (IN)

MACHINE LEARNING BASED SYSTEM(S) FOR NETWORK TRAFFIC DISCOVERY AND ANALYSIS - A simplified explanation of the abstract

This abstract first appeared for US patent application 17957848 titled 'MACHINE LEARNING BASED SYSTEM(S) FOR NETWORK TRAFFIC DISCOVERY AND ANALYSIS

Simplified Explanation

The patent application describes a system for network traffic discovery and analysis, specifically focusing on capturing data traffic across network ports, retrieving source code from code repositories, and determining if the data traffic and source code are associated with API traffic.

• The system determines the first API associated with the API traffic and uses a machine learning subsystem to assess if the API meets supervisory requirements.
• If the first API does not meet supervisory requirements, a remediation protocol is invoked to address the issue.

Potential Applications

This technology could be applied in cybersecurity to monitor and analyze network traffic for potential security threats. It could also be used in software development to ensure compliance with API standards and best practices.

Problems Solved

This technology helps in identifying and addressing potential security vulnerabilities in network traffic. It also assists in maintaining code quality and adherence to API standards in software development.

Benefits

The system provides automated monitoring and analysis of network traffic, reducing the need for manual inspection. It helps in maintaining the security and integrity of network communications and software applications.

Potential Commercial Applications

"Enhancing Network Security and Code Quality through Automated Analysis and Remediation"

Possible Prior Art

There may be prior art related to network traffic analysis tools and API monitoring systems, but specific examples are not provided in the patent application.

How does the machine learning subsystem determine if an API meets supervisory requirements?

The patent application does not provide detailed information on the specific criteria or algorithms used by the machine learning subsystem to assess API compliance.

What types of remediation protocols are invoked when an API does not meet supervisory requirements?

The patent application does not specify the specific actions or protocols that are taken when an API fails to meet supervisory requirements.

Original Abstract Submitted

Systems, computer program products, and methods are described herein for network traffic discovery and analysis. The present invention is configured to capture data traffic across network ports in a computing environment; retrieve source code from code repositories; determine that the data traffic and the source code are associated with application programming interface (API) traffic; determine a first API associated with the API traffic; determine, using a machine learning (ML) subsystem, whether the first API meets supervisory requirements; and invoke a remediation protocol in an instance when the first API does not meet supervisory requirements.