Fairfax Station, VA, United States of America

Kuochu Chang

This inventor holds 2 USPTO granted patents and 1 published patent application. Top assignee: Intelligent Fusion Technology, Inc.. Active years: 2026.

USPTO Granted Patents = 2 

Average Co-Inventor Count = 4.0

ph-index = 1


Company Filing History:


Years Active: 2026

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2 patents (USPTO):Explore Patents

Title: Kuochu Chang: Innovator in Cyber Resilience Technology

Introduction

Kuochu Chang is a notable inventor based in Fairfax Station, VA (US). He has made significant contributions to the field of cybersecurity, particularly in the area of detecting false data injection attacks. His innovative approach has the potential to enhance the reliability of predictive maintenance systems.

Latest Patents

Chang holds a patent for a Cyber Resilience Integrated Security Inspection System (CRISIS) designed to combat false data injection attacks. This method involves collecting sensor data from components monitored by a condition-based predictive maintenance (CBPM) system. It extracts features for a cyberattack detection model and gathers historical data to build a knowledge base about the system. The process combines sensor data and historical data to train the detection model. A graphical Bayesian network model is utilized to capture domain knowledge and condition-symptom relationships. Ultimately, this system detects false data injection attacks on the CBPM system effectively.

Career Highlights

Chang is currently employed at Intelligent Fusion Technology, Inc., where he continues to develop innovative solutions in cybersecurity. His work focuses on enhancing the security and resilience of critical systems against cyber threats.

Collaborations

Chang collaborates with Sixiao Wei, contributing to advancements in their field through shared expertise and innovative ideas.

Conclusion

Kuochu Chang's work in developing methods to detect false data injection attacks represents a significant advancement in cybersecurity. His contributions are vital for improving the integrity of predictive maintenance systems.

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