SMART MALWARE DEFENSE: MACHINE LEARNING TECHNIQUES FOR RANSOMWARE CLASSIFICATION AND PREDICTION

Abstract

Ransomware has emerged as one of the most destructive forms of cyberattacks, encrypting user data and demanding payment to restore access. The increasing sophistication and frequency of ransomware variants pose major challenges to traditional signature-based security systems, which often fail to detect new or evolving threats. To address these limitations, this research proposes a smart malware defense framework that leverages machine learning techniques for accurate ransomware classification and earlystage detection. The proposed model employs a combination of static and dynamic feature analysis, extracting behavioral patterns such as file system activity, API calls, and registry modifications to differentiate ransomware from benign software. Various supervised learning algorithms, including Random Forest, Support Vector Machine (SVM), and Gradient Boosting, are trained and evaluated to identify the most effective approach for ransomware detection. Feature selection techniques are applied to optimize performance by eliminating redundant attributes and enhancing model interpretability. Experimental results demonstrate that machine learning-based detection significantly improves accuracy and reduces false positives compared to conventional anti-malware systems. The framework effectively classifies unseen ransomware families and predicts their malicious intent with minimal computational overhead. This approach not only provides a robust defense mechanism against ransomware attacks but also establishes a foundation for adaptive and intelligent cybersecurity systems capable of learning from emerging threats