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This paper focuses on the anticipatory enhancement of methods of detectingstealth software. Cyber security detection tools are insufficiently powerful toreveal the most recent cyber-attacks which use malware. In this paper, we willpresent first an idea of the highest stealth malware, as this is the mostcomplicated scenario for detection because it combines both existinganti-forensic techniques together with their potential improvements. Second, wepresent new detection methods, which are resilient to this hidden prototype. Tohelp solve this detection challenge, we have analyzed Windows memory contentusing a new method of Shannon Entropy calculation; methods of digitalphotogrammetry; the Zipf Mandelbrot law, as well as by disassembling the memorycontent and analyzing the output. Finally, we present an idea and architectureof the software tool, which uses CUDA enabled GPU hardware to speed-up memoryforensics. All three ideas are currently a work in progress.

Acceleration of Statistical Detection of Zero-day Malware in the Memory Dump Using CUDA-enabled GPU Hardware