Distributed machine learning for anomaly detection

US11362910B2 · kind B2 · utility

Assignee

• International Business Machines Corporation · US

Inventors

• Jian Lin · Alpharetta, US
• Matthew Elsner · Dunwoody, US
• Ronald B. Williams · Austin, US
• Michael Josiah Bolding · Smyrna, US
• Yun Pan · Roswell, US
• Paul S. Taylor · Redwood City, US
• Cheng-Ta Lee · Taipei, TW

Key dates

• Filing date: Jul 17, 2018
• Grant date: Jun 14, 2022
• Expiry date: Dec 11, 2040

Classification

• Technology area (CPC H): Electricity
• CPC primary: H04L63/1433
• WIPO field: Digital communication
• WIPO sector: Electrical engineering

Abstract

A tiered machine learning-based infrastructure comprises a first machine learning (ML) tier configured to execute within an enterprise network environment and that learns statistics for a set of use cases locally, and to alert deviations from the learned distributions. Use cases typically are independent from one another. A second machine learning tier executes external to the enterprise network environment and provides further learning support, e.g., by determining a correlation among multiple independent use cases that are running locally in the first tier. Preferably, the second tier executes in a cloud compute environment for scalability and performance.