Investigating Malware Behavior in the Windows Operating System Using Reverse Engineering and Pattern Recognition Methods

An empirical study conducted on primary malware dataset for investigating behavioural analysis. This study propose a novel reverse engineering approach incorporated with CNN. There is no study that has analyzed malware behavior using reverse engineering techniques incorporating CNN. Hence, We developed novel approach to overcome this issue. The results revealed that our study able to analysed malware behavior effectively with 96% accuracy. We includes the investigation of malware behavior targeting windows operating system and excludes malware that affects other operating systems, encrypted malware and limits its focus to static analysis without employing dynamic analysis. This study demonstrates the successful outcomes and the developed approach achieved a high detection accuracy, highlighting its potential to enhance exiting malware detection approach.本研究基于核心恶意软件数据集开展实证研究，旨在探究恶意软件行为分析相关技术。本研究提出一种结合卷积神经网络（Convolutional Neural Network, CNN）的新型逆向工程方法。目前尚无研究借助结合卷积神经网络的逆向工程技术开展恶意软件行为分析。据此，我们开发了全新的研究方法以填补该领域空白。实验结果表明，本方法可有效分析恶意软件行为，准确率达96%。本研究仅针对Windows操作系统下的恶意软件行为展开分析，排除了影响其他操作系统的恶意软件与加密型恶意软件，且仅采用静态分析手段，未涉及动态分析。本研究验证了所提方案的有效性，所开发的方法实现了较高的检测准确率，凸显了其在优化现有恶意软件检测方法方面的应用潜力。