Opcode-Based Ransomware Detection Using Hybrid Machine Learning Algorithms

Ransomware has become an increasingly pervasive threat to cybersecurity, capable of causing severe operational disruptions and financial losses across various sectors. A novel approach is introduced through opcode analysis and a hybrid combination of machine learning algorithms, offering a significant improvement in detecting ransomware over traditional methods. By utilizing the low-level opcode sequences extracted from binaries, the system captures resilient features that are not easily manipulated through obfuscation techniques, making it more robust against evolving ransomware strains. The proposed framework integrates Random Forest, XGBoost, and Support Vector Machines (SVM), each contributing distinct strengths to the overall detection process. Experimental results highlight that the system achieved high accuracy, precision, and recall, outperforming existing detection methods. Furthermore, the use of opcode-based features enables the models to generalize well across various ransomware families, ensuring reliability in realworld cybersecurity applications. The research demonstrates the effectiveness of machine learning in addressing the rapidly evolving nature of ransomware, providing an efficient, scalable, and highly accurate detection mechanism.