Integrative Malware Detection using Ensemble Learning

Employed artificial intelligence techniques, such as GBM, XGBoost, CatBoost, and LightGbm ensemble learning models, to identify malware families.

• Used the dataset from “Microsoft Malware Classification Challenge2015”.
• Examined and assessed the effectiveness of many ensemble learning models for the identification and categorisation of malware
• Used for identifying hybrid malware families reducing potential Cyber Security risks.

Symbi Stock Exchange

A Stock Management System for expensive metal commodities like Gold, Silver, Nickel, Aluminium etc.

• Designed to trade precious metal commodities such as Gold, Silver, Crude Oil, Copper, Zinc, and Nickel, it is supported by a strong MySQL-developed backend.
• Developed an easy-to-use interface that is both responsive and intuitive, with interactive Java Swing components, secure password validation, and reliable performance for traders.
• Performed migration and enhancements for merchandising dashboard, involved data integration and its feature improvement.

Bachelor of Technology

Grade: 7.6 CGPA.

Higher Secondary School

Grade: 98.4%.