ASSCA: API sequence and statistics features combined architecture for malware detection
作者： Lu Xiaofeng , Jiang Fangshuo , Zhou Xiao , Yi Shengwei , Sha Jing
DOI: 10.1016/J.COMNET.2019.04.007
关键词:
摘要: Abstract In this paper, a new deep learning and machine combined model is proposed for malware behavior analysis. One part of it analyzes the dependency relation in API (Application Programming Interface) call sequence at functional level, extracts features random forest to learn classify. The other employs bidirectional residual neural network study discover with redundant information preprocessing. sequence, future much more important conjecturing semantic current call. We conducted experiments on dataset. experiment results show that both methods can effectively detect malwares. However, framework has better classification performance. accuracy detection architecture 0.967.
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