An Opcode Sequences Analysis Method For Unknown Malware Detection
Zhi Jie Sun, Zhihong Rao, Jianfeng Chen, Rui Xu, Da He, Hui Yang, Jie Liu · 2019
One of the main challenges in security today is defending against unknown malware attacks which have the potential to harm a computer or network. Hence, detecting malware has become one of the most important challenges for the security of computer systems. The known malware detection methods based on the appearance of opcode sequences has to construct a matrix from programs of different architectures to extract high-level features. In order to resolve high dimensional inputs vector and differences assembly instruction, this paper proposes a novel method for detecting static characteristics of 32-bit and 64-bit malicious Portable Executable (PE) Windows files by opcode sequences analysis. By compute the frequency of occurrence of each opcode sequence and distinguishing different types of 32-bit and 64-bit PE files, the proposed method shows promising results with less complexity in comparison with previous studies, which is beneficial to train machine learning model such as k-nearest neighbor (KNN) and back-propagation neural network (BP). Our method is evaluated on more than 20,000 samples, and experimental results show that our system can effectively detect and classify unknown malware.