Dynamic malware analysis has become popular because it allows analysts to observe the behavior of running samples, facilitating improved decisions for malware detection and classification. With the increasing number of new malware, there is a growing need for an automated malware analysis engine that can accurately detect malware samples. In this paper, we briefly introduce the malware detection and classification approaches. Furthermore, we introduce a new malware detection and classification framework that works specifically in the dynamic analysis setting, namely Incremental Malware Detection and Classification Framework, or IMDCF. In this paper, we present a novel framework designed specifically for the dynamic analysis setting, named the Incremental Malware Detection and Classification Framework (IMDCF). IMDCF provides a end-to-end solution for general-purpose malware detection and classification with 96.49\% accuracy and simple architecture.
翻译:动态恶意软件分析因其使分析员能够观察运行样本的行为,从而有助于恶意软件检测和分类而变得流行。随着新恶意软件数量的增加,需要自动化的恶意软件分析引擎以准确地检测恶意软件样本。我们在本文中简要介绍了恶意软件检测和分类方法。此外,我们引入了一种新的基于动态分析设置的恶意软件检测和分类框架,即增量恶意软件检测和分类框架(IMDCF)。本文介绍了一种针对动态分析设置的新型框架,称为增量恶意软件检测和分类框架(IMDCF)。IMDCF提供了96.49%的准确性和简单的结构,为通用恶意软件检测和分类提供了一种端到端的解决方案。