A malware family classification method based on Efficient-Net and 1D-CNN fusion is proposed. Given the problem that some local information of malware itself as one-dimensional data will be lost when the malware is imaged, the malware is converted into an image and one-dimensional vector and then input into two neural networks. The network of two-dimensional convolution architecture is used to extract the texture features of malware, and the one-dimensional convolution is used to extract the features of local adjacent information, the deep characteristics of different networks are fused, and the two networks are modified at the same time during backpropagation. This method not only extracts the texture features of malware but also saves the features of the malware itself as one-dimensional data, which shows better performance for multiple datasets.

中文翻译：

---

基于 Efficient-Net 和 1D-CNN 融合的恶意软件家族分类

提出了一种基于 Efficient-Net 和 1D-CNN 融合的恶意软件家族分类方法。鉴于恶意软件本身作为一维数据的一些局部信息在对恶意软件进行成像时会丢失的问题，将恶意软件转换为图像和一维向量，然后输入到两个神经网络中。二维卷积架构的网络用于提取恶意软件的纹理特征，一维卷积用于提取局部相邻信息的特征，融合不同网络的深层特征，对两个网络进行修改同时在反向传播期间。该方法不仅提取了恶意软件的纹理特征，而且将恶意软件本身的特征保存为一维数据，在多个数据集上表现出更好的性能。