Behavioral analysis of malware using sandboxing techniques

This research paper investigates the dynamic behavioral analysis of Windows-based Portable Executable (PE) malware samples using sandboxing techniques. The study focuses on comparing various sandboxing methodologies with an emphasis on their ability to detect sophisticated malware behaviors in a controlled environment. In particular, techniques such as the incorporation of realistic user behavior emulation and the integration of machine learning with sandbox environments are examined. The methodology involves deploying agent-based and agent-less sandbox systems to monitor malware execution and capturing system interactions. The results underscore the effectiveness of advanced sandboxing techniques in mitigating evasion tactics deployed by modern malware. Moreover, the paper discusses recent trends that integrate artificial intelligence to further enhance detection accuracy. Overall, the paper asserts that while agent-based approaches generally perform better in terms of comprehensive behavior capture, the evolution in sandboxing designs, notably with user behavior emulation and machine learning integration, significantly improves malware detection outcomes.