18651009. Custom Patching Automation with Machine Learning Integration simplified abstract (BANK OF AMERICA CORPORATION)
Contents
Custom Patching Automation with Machine Learning Integration
Organization Name
Inventor(s)
Syed Luqman Ahmed of Khanpur (IN)
Adi Narayana Rao Garaga of Hyderabad (IN)
Custom Patching Automation with Machine Learning Integration - A simplified explanation of the abstract
This abstract first appeared for US patent application 18651009 titled 'Custom Patching Automation with Machine Learning Integration
Simplified Explanation: A machine learning computing system identifies and patches vulnerabilities in a server, creating, validating, and deploying patch jobs based on a knowledge base.
Key Features and Innovation:
- Machine learning system identifies server vulnerabilities
- Schedules patching intervals in a centralized tracking module
- Creates, validates, and deploys patch jobs based on knowledge base
- Monitors patch job status and updates user interface module
- Generates assessment reports for executed patch jobs
- Updates knowledge base for future decision-making
- Automatically reschedules patching intervals upon failure indication
Potential Applications: This technology can be applied in various industries where server security is crucial, such as IT, cybersecurity, and network management.
Problems Solved: This technology addresses the challenge of efficiently identifying and patching vulnerabilities in servers to enhance overall security and reduce the risk of cyber threats.
Benefits:
- Improved server security
- Efficient patching process
- Automated scheduling and monitoring
- Enhanced decision-making based on assessment reports
Commercial Applications: The technology can be utilized by IT companies, cybersecurity firms, and organizations with large server networks to streamline patching processes and enhance overall security measures.
Prior Art: Readers can explore prior research on machine learning-based vulnerability detection and patching systems in the fields of cybersecurity and network management.
Frequently Updated Research: Stay informed about the latest advancements in machine learning algorithms for vulnerability detection and patching in server systems.
Questions about Machine Learning Computing System for Server Vulnerability Patching: 1. How does the machine learning system prioritize vulnerabilities for patching? 2. What are the key components of the knowledge base used for creating patch jobs?
Original Abstract Submitted
A machine learning computing system identifies a vulnerability associated with a server. Based on information associated with the server and a knowledge base, the computing system schedules an interval for patching the server in a centralized tracking module. Based on the knowledge base and the vulnerability, the computing system creates, validates, and deploys the patch job. During patch job execution, the computing system monitors the status of the patch job at the server and transmits status updates to a user interface module. After expiration of the interval, the computing system generates an assessment report for the executed patch job. The computing system updates the knowledge base based on the assessment report to improve future decisioning processes. Based on the success or failure of the patch job, the computing system, upon a failure indication, automatically reschedules an interval for patching the server.
