Jason K Ellis

Title: Jason K Ellis: Innovator in Anomalous Behavior Detection

Introduction

Jason K Ellis is a prominent inventor based in Bellevue, WA (US). He has made significant contributions to the field of technology, particularly in the detection of anomalous behavior within systems and networks. His innovative approach combines data analysis with advanced neural network techniques.

Latest Patents

Jason K Ellis holds a patent for "Systems and methods for detecting anomalous system or network behavior." This patent describes a system and associated methods for detecting anomalous behavior in a system. In some embodiments, time-series data obtained from the system, such as log data, is used as input to a process that converts the data into greyscale values. These greyscale values are then utilized to construct an 'image' of the system operation, which serves as input to a convolutional neural network (CNN). The CNN is trained to recognize when other input 'images' constructed from time-series data are anomalous or indicative of a difference between prior and current operations of the system.

Career Highlights

Jason is currently employed at Dimensional Mechanics, Inc., where he continues to develop innovative solutions in the field of system behavior analysis. His work has garnered attention for its potential applications in various industries, enhancing the ability to detect and respond to system anomalies effectively.

Collaborations

Jason collaborates with Rajeev Dutt, leveraging their combined expertise to push the boundaries of technology in their field.

Conclusion

Jason K Ellis is a notable inventor whose work in detecting anomalous behavior in systems showcases the intersection of data science and machine learning. His contributions are paving the way for advancements in technology that can significantly improve system reliability and security.