Neural Dissection for Ransomware Detection Using Dynamic Opcode Transition Matrices
Bruce Oliver, Ann Montague, Richard Wentworth, Jeffrey Langley · 2024
An ever escalating sophistication of malicious software calls for innovative detection methodologies. Traditional signature-based mechanisms often falter against rapidly evolving threats, showing the need for adaptive solutions. This research introduces Dynamic Opcode Transition Matrices (DOTM), a novel approach that scrutinizes the sequence of operational codes executed by software to identify anomalous patterns indicative of malicious activity. By constructing matrices that represent opcode transitions, DOTM captures the behavioral essence of software, facilitating the differentiation between benign and malicious entities. Comprehensive evaluations demonstrate that DOTM achieves superior detection accuracy compared to conventional methods, exhibiting resilience against obfuscation techniques commonly employed by adversaries. The integration of DOTM into existing cybersecurity frameworks promises to enhance the robustness of defense mechanisms, offering a proactive stance against emerging threats. The findings demonstrate the potential of opcode-based analysis as a cornerstone in the development of next-generation cybersecurity solutions.