Auto-detection of sophisticated malware using lazy-binding control flow graph and deep learning

In this paper, we propose an enhanced form of CFG, known as lazy-binding CFG to reflect the DEC behaviors. Then, with the recent advancement of the deep learning techniques, we present a method of producing image-based representation from the generated CFG. As deep learning is very popular to perform image classification on very large dataset, our proposed technique can be applied for malware detection on real-world computer programs and thus enjoying very high accuracy. We also illustrate our analysis results with some well-known malware samples, including WannaCry, Kasperagent and Sality, one of the most sophisticated polymorphic viruses.