In the era of the internet and smart devices, the detection of malware has become crucial for system security. Malware authors increasingly employ obfuscation techniques to evade advanced security solutions, making it challenging to detect and eliminate threats. Obfuscated malware, adept at hiding itself, poses a significant risk to various platforms, including computers, mobile devices, and IoT devices. Conventional methods like heuristic-based or signature-based systems struggle against this type of malware, as it leaves no discernible traces on the system. In this research, we propose a simple and cost-effective obfuscated malware detection system through memory dump analysis, utilizing diverse machine-learning algorithms. The study focuses on the CIC-MalMem-2022 dataset, designed to simulate real-world scenarios and assess memory-based obfuscated malware detection. We evaluate the effectiveness of machine learning algorithms, such as decision trees, ensemble methods, and neural networks, in detecting obfuscated malware within memory dumps. Our analysis spans multiple malware categories, providing insights into algorithmic strengths and limitations. By offering a comprehensive assessment of machine learning algorithms for obfuscated malware detection through memory analysis, this paper contributes to ongoing efforts to enhance cybersecurity and fortify digital ecosystems against evolving and sophisticated malware threats. The source code is made open-access for reproducibility and future research endeavours. It can be accessed at https://bit.ly/MalMemCode.

本研究通过内存转储分析，利用多样的机器学习算法提出了一种简单且具有成本效益的混淆恶意软件检测系统。通过评估决策树、集成方法和神经网络等多种机器学习算法在内存转储中检测混淆恶意软件的效果，研究涵盖了多个恶意软件类别，为增强网络安全并抵御不断演变和复杂化的恶意软件威胁提供了新的洞见。

混淆恶意软件检测：通过内存分析研究真实场景