Identification of malware mimicry attacks using process escalating visualization

The cybercrime industry is increasingly evolving. The cost of cybercrime showed up as the third largest economy after the USA and China. Malware is considered one of the biggest threats that the world faces. The rapid evolution in the malware industry should be met with new detection models that are able to understand the malicious process. This paper proposes an approach that identifies malicious processes based on visualising their progressive execution. Moreover, formulating a heuristic function that identifies malicious mimicry processes that can fake antivirus by showing up as benign or normal processes Our proposed model showed a comparative accuracy score compared to other peer approaches.