Artificial neural network for decision of software maliciousness

With the rapidly development of virus technology, the number of malicious code has continued to increase. So it is imperative to optimize the traditional manual analysis method by automatic maliciousness decision system. Motivated by the inference technique for detecting viruses, and a recent successful classification method, we explore Radux-an automatic software maliciousness decision system. It rests on artificial neural network based on behavior hidden in malicious code. Decompile technique is applied to characterize behavioral and structural properties of binary code, which creates more abstract descriptions of malware. Experiment shows that this system can decision software maliciousness efficiently.