Advanced Ransomware Detection and Classification via Semantic Analysis of Memory Opcode Patterns
Thomas Lowev, Charlotte Fisher, James Collins · 2024
Ransomware attacks have become one of the most prevalent and damaging forms of cyber threats, with their increasing sophistication posing significant challenges to traditional detection methodologies. The novel approach presented in this research leverages the semantic analysis of memory opcode patterns, introducing a more granular and insightful method for detecting and classifying ransomware. By applying advanced machine learning models, particularly deep learning architectures like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), the research demonstrates a significant improvement in the accuracy and reliability of ransomware detection. The proposed models effectively capture the complex semantic relationships within opcode sequences, allowing for the precise identification and classification of ransomware across various families. The evaluation of these models against existing detection techniques shows that the integration of semantic analysis into the detection process leads to higher accuracy, precision, and recall, highlighting the potential of this approach to address the evolving nature of ransomware threats. The study also identifies key challenges, including the computational demands of deep learning models and the difficulty in distinguishing between closely related ransomware and benign software, offering insights for future research and development in this critical area of cybersecurity.