Jeong Been Park  Kyung Soo Han  Tae Gune Kim  Eul Gyu Im

Malware analysis Malware classification opcode Decision Tree Static Analysis attribute selection

Recently, the number of new malware and malware variants has dramatically increased. As a result, the time for analyzing malware and the efforts of malware analyzers have also increased. Therefore, malware classification helps malware analyzers decrease the overhead of malware analysis, and the classification is useful in studying the malware’s genealogy. In this paper, we proposed a set of key opcode to classify the malware. In our experiments, we selected the top 10-opcode as key opcode, and the key opcode decreased the training time of a Supervised learning algorithm by 91% with preserving classification accuracy.