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. Deeompile 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.
artificial neural network maliciousness decision software behavior
Zhang Yichi Pang Jianmin Zhao Rongcai Guo Zhichang
National Digital Switching System Engineering & Technology Research Center Zhengzhou, China
国际会议
厦门
英文
622-625
2010-10-29（万方平台首次上网日期，不代表论文的发表时间）