MUMBAI, India, July 24 -- Intellectual Property India has published a patent application (202641085302 A) filed by Jntuh University College Of Engineering on July 12, 2026, for Ensemble Learning For Enhanced Malware Detection.
Inventors include Dr. K. Santhi Sree; C Shiva Rama Krishna; Kathi Mounika; Pedapalli Sobhitha; Mellacheruvu Venkata Gaayatri; Ujjelli Lokesh Reddy; M. Srimani; Gurri Suvidha Reddy; Erri Suvarna; Hanumandla Naga Shiva; Chirra Karthik; V. Nithin; and Kayam Sravani.
The application for the patent was published on July 17, 2026, under issue no. 29/2026.
Abstract: The present invention discloses an ensemble learning-based malware detection framework configured to accurately classify malicious and benign software samples using multiple machine learning techniques. The framework comprises a Data Collection Module for acquiring malware datasets, a Data Preprocessing Module for data cleaning, duplicate removal, label encoding, and feature scaling, a Feature Engineering Module for feature extraction and feature selection, an Ensemble Model Training Module configured to train Random Forest, AdaBoost, Gradient Boosting, and Stacking classifiers, a Malware Detection and Classification Module for predicting malware and benign software, a Performance Evaluation Module for computing accuracy, precision, recall, F1-score, and confusion matrices, and a User Interface Module for displaying prediction results and comparative model analysis. The integrated use of bagging, boosting, and stacking techniques improves malware detection accuracy, reduces misclassification, enhances robustness, and enables reliable detection of known and unknown malware samples for cybersecurity applications while maintaining scalable and efficient malware classification performance.
Disclaimer: Curated by HT Syndication.
