Malicious behavior pattern mining using control flow graph

Cyber hacking attacks based on malicious code are becoming diversified. Malicious code analysis is very important because static flow analysis can naturally be helpful as part of the detection process given that malicious codes can affect the data and control flow of a program. This paper introduces the representation method of the Control Flow Graph based on malicious codes. Our proposed method can detect well-known malicious codes and their variants. In addition, the proposed method shows a new response method through the conceptual approach method of source codes.