  The popularity of dynamic malware analysis has grown significantly, as it
enables analysts to observe the behavior of executing samples, thereby
enhancing malware detection and classification decisions. With the continuous
increase in new malware variants, there is an urgent need for an automated
malware analysis engine capable of accurately identifying malware samples. In
this paper, we provide a brief overview of malware detection and classification
methodologies. Moreover, we introduce a novel framework tailored for the
dynamic analysis environment, called the Incremental Malware Detection and
Classification Framework (IMDCF). IMDCF offers a comprehensive solution for
general-purpose malware detection and classification, achieving an accuracy
rate of 96.49% while maintaining a simple architecture.
