A Novel Method for Anomaly Detection Using Scan Statistics

Overview of Technology Potential methods for detecting clusters/anomalies while simultaneously dealing with model misspecification Description of Technology  Mississippi State researchers have developed scan-based methods to identify and statis…

Overview of Technology

Potential methods for detecting clusters/anomalies while simultaneously dealing with model misspecification

Description of Technology 

Mississippi State researchers have developed scan-based methods to identify and statistically quantify abnormal and potentially adversarial behavior. The researchers propose to effectively extend the usefulness of scan-based statistics to a multitude of applied settings. 

The goal is to identify anomalous activity while also allowing for a quantifiable degree of misspecification of normal activity. They present a novel tool called theta-filtering for scan-based methods. In essence, theta-filtering presents a host of potential methods for detecting clusters/anomalies while simultaneously dealing with model misspecification. 

Applications

Fraudulent activities have caused losses of multi-million dollars in credit card, retail, service, and government businesses. 

This invention is a novel statistical technique that is tailored to statistically identify and quantify clusters or anomalous activities as quickly as possible. 

Seeking

  • Development partner
  • Commercial partner
  • Licensing

IP Status

  • No patent

Website

https://msstate-innovations.technologypublisher.com/technology/46122

Contact Information

TTO Home Page: https://msstate-innovations.technologypublisher.com

Name: Jeremy Clay

Title: Director

Department: Office of Technology Management

Email: jmc17@msstate.edu