MUMBAI, India, Sept. 14 -- Intellectual Property India has published a patent application (202621090700 A) filed by Dr. Vandana Shakya; Dr. Chetna Devkar; Sarita Sahni; Dr. Nikhil Nigam; Ajay Sahu; Abhishek Jadhav; and Rahul Singh on July 25, 2026, for Machine Learning Enabled Secure Authentication System.
Inventors include Dr. Vandana Shakya; Dr. Chetna Devkar; Sarita Sahni; Dr. Nikhil Nigam; Ajay Sahu; Abhishek Jadhav; and Rahul Singh.
The application for the patent was published on September 11, 2026, under issue no. 37/2026.
Abstract: Abstract The present invention relates to a Machine Learning Enabled Secure Authentication System configured to provide intelligent, adaptive, and continuous authentication for digital platforms. The system collects authentication information including behavioral biometrics, physiological biometrics, device information, contextual parameters, and historical authentication records. The collected data is preprocessed and transformed into authentication features that are analyzed using one or more machine learning models to generate an authentication confidence score. A dynamic risk assessment module evaluates contextual anomalies and security risks associated with each authentication request. Based on the calculated confidence and risk levels, an adaptive authentication controller selectively grants access, requests additional authentication factors, performs continuous session monitoring, or blocks unauthorized access attempts. A continuous learning module periodically retrains the machine learning models using updated authentication records to improve accuracy over time. The invention enhances cybersecurity, minimizes false authentication decisions, improves user convenience, and provides scalable authentication across enterprise networks, cloud computing platforms, financial systems, healthcare applications, and Internet of Things environments.
Disclaimer: Curated by HT Syndication.
