Software-defined forensic framework for malware disaster management in Internet of Thing devices for extreme surveillance

Abstract

Malware perception is an important technique which has to be explored to analyze the corpus amount of malware in short duration for effective disaster management. Accurate analyses of malware must be done by detecting them in initial stage in an automatic way to avoid severe damage in Internet of Thing devices. This is enabled by visualizing malware by using a software-defined visual analytic system. Though many auto analysis techniques are present visualization of malware is one of the effective techniques preferred for large analysis. Malware exhibits malicious behavior on computing devices by installing harmful software such as viruses. The existing static and dynamic form of malware detection is an inefficient technique as it involve in disassembling of malicious code. In this project, the visualization of malware in the form of images is proposed in order to find the malicious insertion on the executable files of computing devices for extreme surveillance. The malware detection becomes easier to visualize the malicious behavior in form of images by feature based classification of images as the global property of exe gray scale image is unchanged. This will be an eye open in healing the security issues in cyber-crime and provide extreme surveillance.