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A Novel Malware Detection and Family Classification Scheme for IoT Based on DEAM and DenseNet
2021
Security and Communication Networks
With the rapid increase in the amount and type of malware, traditional methods of malware detection and family classification for IoT applications through static and dynamic analysis have been greatly challenged. In this paper, a new simple and effective attention module of Convolutional Neural Networks (CNNs), named as Depthwise Efficient Attention Module (DEAM), is proposed and combined with a DenseNet to propose a new malware detection and family classification model. Based on the good
doi:10.1155/2021/6658842
fatcat:u2lplch3ardynn2tggpdxca7gy