Malware analysis is the process of determining the origin of the malware sample and its potential impacts. Malware acts like worms, viruses, Trojan, bugs, adware, spyware, and any suspicious software that harms the system. Dynamic malware analysis runs systematically during the execution in the controlled environment, observing the behavior of the malware sample to remove the infection or avoid further spreading into other systems. Hence, developing dynamic malware analysis tools and techniques has become an emerging research area in malware forensics.
   Dynamic analysis models assist in extracting the intrigue information from the suspicious data against malicious activities concerning the predefined rules. In contrast to the static analysis, dynamic malware analysis provides robust results in a better understanding of suspicious behavioral patterns. Although, conventional dynamic analysis tools and techniques are inadequate for analyzing the coverage of the different aspects of the malware behavior.
   Recently, malware forensics deals with the new types of malware of crypto miners and ransomware in a dramatically changing environment. The computing environments of mobile, cloud, and Internet of Things (IoT) increasingly suffer from the different new malware. Hence, machine learning and deep learning algorithms have been increasingly adopted by the different computing environments over the changes in cyberspace criminal activities.