In recent years, malware becomes more threatening. Concerning the increasing malware variants, there comes Machine Learning (ML)-based and Deep Learning (DL)-based approaches for heuristic detection. Nevertheless, the prediction accuracy of both needs to be improved. In response to the above issues in the PE malware domain, we propose the DL-based approaches for detection and use static-based features fed up into models. The contributions are as follows: we recapitulate existing malware detection methods. That is, we propose a vec-torized representation model of the malware instruction layer and semantic layer based on Glove. We implement a neural network model called MCC_RCNN (Malware Detection and Recurrent Convolutional Neural Network), comprising of the combination with CNN and RNN. Moreover, we provide a description of feature fusion in static behavior levels. With the numerical results generated from several comparative experiments towards evaluating the Glove-based vectoriza-tion, MCC_RCNN-based classification methodology and feature fusion stages, our proposed classification methods can obtain a higher prediction accuracy than the other baseline methods.

中文翻译：

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用于PE恶意软件检测的新颖DL方法：探索Glove向量化，MCC_RCNN和特征融合

近年来，恶意软件变得更具威胁性。关于不断增长的恶意软件变体，有基于机器学习（ML）和基于深度学习（DL）的启发式检测方法。但是，两者的预测精度都需要提高。针对PE恶意软件领域中的上述问题，我们提出了基于DL的方法来进行检测并使用馈入模型的基于静态的功能。贡献如下：我们概述了现有的恶意软件检测方法。也就是说，我们提出了基于手套的恶意软件指令层和语义层的向量化表示模型。我们实现了一个称为MCC_RCNN（恶意软件检测和递归卷积神经网络）的神经网络模型，其中包括与CNN和RNN的组合。此外，我们提供了静态行为级别中特征融合的描述。通过一些比较实验得出的数值结果，这些评估结果用于评估基于Glove的矢量化，基于MCC_RCNN的分类方法和特征融合阶段，因此，与其他基准方法相比，我们提出的分类方法可以获得更高的预测精度。