Detecting Malicious Assembly with Deep Learning
IEEE National Aerospace and Electronics Conference(2018)
摘要
We present our work on evaluating the usefulness of deep, convolutional neural networks (DNN) for classifying assembly or machine code as malicious or benign. Our results show that a DNN trained on a small dataset showed 95.1% accuracy in program classification. We also show a modified network can achieve 88% accuracy in classifying nine types of malware on a larger dataset, leaving room for future work to address variable length files.
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关键词
deep learning,convolutional neural networks,DNN,classifying assembly,program classification,malicious assembly,machine code,deep neural networks,malware
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