Open AccessMalware Detection Model Based on Deep Convolution Generation Adversarial Network
Author(s) -
Cong Tang,
Jiangyong Shi,
Yi Yang,
Yuexiang Yang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1738/1/012110
Subject(s) - malware , computer science , discriminator , artificial intelligence , convolution (computer science) , countermeasure , computer security , pattern recognition (psychology) , data mining , machine learning , algorithm , artificial neural network , engineering , detector , telecommunications , aerospace engineering
At present, malware is one of the biggest threats to Internet security. In this paper, a new static malware analysis algorithm MSG is proposed based on DCGAN. The algorithm transforms the disassembled malware code into a gray image based on SimHash, and uses DCGAN to generate countermeasure samples for training to detect unknown malware variants. The experimental results show that the detection rate of our algorithm for malware can reach 96.67%, and the dodge rate of generated malicious samples can reach 0.92 under the detection of CNN discriminator.