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.
Detecting Malicious Assembly with Deep Learning
2018-07-01
103816 byte
Conference paper
Electronic Resource
English