Project Ire

Autonomous malware classification

微软研究院博客

Stylized digital illustration of a multi-layered circuit board. A glowing blue microchip sits at the top center, with intricate circuitry radiating outward. Beneath it, four stacked layers transition in color from blue to orange, each featuring circuit-like patterns. Smaller rectangular and circular components are connected around the layers, all set against a dark background with scattered geometric shapes.

微软研究院博客

Project Ire autonomously identifies malware at scale

2025年8月5日 | Brian Caswell, Dustin Fraze, Sarah Smith, Rodrigo Racanicci, Tim Middleton-Sally, Shelby Hayes, Stanley He, Katy Smith, Bhakta Pradhan, 和 Mike Walker

Designed to classify software without context, Project Ire replicates the gold standard in malware analysis through reverse engineering. It streamlines a complex, expert-driven process, making large-scale malware detection faster & more consistent.

人员

Brian Caswell

Principal Security Engineer

Dustin Fraze

Principal Research Scientist

Sarah Smith

Senior Program Manager

Rodrigo Racanicci

Principal Software Engineer

Tim Middleton-Sally

Senior Security Researcher

Shelby Hayes

Senior Technical Program Manager

Stanley He

Senior Security Researcher

Katy Smith

Data Scientist II

Bhakta Pradhan

Senior Security Researcher

Mike Walker

Research Manager

Cory Duplantis

Bob Fleck

Senior Security Engineer

Stylized digital illustration of a multi-layered circuit board. A glowing blue microchip sits at the top center, with intricate circuitry radiating outward. Beneath it, four stacked layers transition in color from blue to orange, each featuring circuit-like patterns. Smaller rectangular and circular components are connected around the layers, all set against a dark background with scattered geometric shapes.

Blog Project Ire autonomously identifies malware at scale

Designed to classify software without context, Project Ire replicates the gold standard in malware analysis through reverse engineering. It streamlines a complex, expert-driven process, making large-scale malware detection faster & more consistent.