Published Paper Details:

Ransomware has emerged as one of the most dam- aging cyber threats, targeting personal, enterprise, and critical infrastructure systems by encrypting data and demanding ran- som payments. Signature-based antivirus solutions fail to detect modern ransomware variants due to polymorphism, encryption, and obfuscation techniques. To address these limitations, this paper proposes a hybrid stacked machine learning framework for ransomware detection and classification. The proposed system integrates static and dynamic analysis to extract discriminative features such as Portable Executable headers, entropy values, API calls, file system operations, registry modifications, and network behavior. Feature selection is performed using an Extra Tree Classifier to reduce dimensionality and improve learning efficiency. A stacked ensemble architecture combining Extra Tree Classifier as the base learner and Logistic Regression as the meta learner is employed to enhance classification performance while maintaining interpretability and low computational overhead. The system is deployed using a Flask-based web interface for real-time file analysis. Experimental results demonstrate that the proposed approach achieves 98.2% accuracy with 1.2% false positive rate, outperforming traditional machine learning models and providing competitive performance compared to deep learning approaches with significantly lower computational cost, making it suitable for practical ransomware detection.