Malware Detection Using 1-Dimensional Convolutional Neural Networks
Malware Detection Using 1-Dimensional Convolutional Neural Networks
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DOI：
10.1109/eurospw.2019.00034
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发表时间：
2019-06
期刊：
影响因子：
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通讯作者：
Arindam Sharma;P. Malacaria;M. Khouzani
中科院分区：
文献类型：
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作者：
Arindam Sharma;P. Malacaria;M. Khouzani
这项工作介绍了一种基于一维卷积神经网络的高精度、高效的恶意软件检测系统。系统将二进制文件作为输入，并将其分类为恶意文件或良性文件。对二进制文件进行最少的预处理，在训练期间将特征发现留给网络。与其他基于卷积神经网络（CNN）的方法的一个关键区别是使用一维卷积；这种方法选择对探测器具有显着的积极影响。为了将检测器与最先进的技术进行比较，还实现了基于 TF-IDF 的基准恶意软件检测器：实验表明，所提出的 CNN 检测器在保持相似训练时间的同时提高了准确性。该系统还在包含 11130 个二进制文件的公开数据集上与现有的基于嵌入的 CNN 检测器进行了比较。所提出的系统在准确性和训练时间上都优于基于嵌入的 CNN。
This work introduces a highly accurate and efficient malware detection system based on 1-dimensional convolutional neural networks. The system takes as input a binary file and classifies it as malicious or benign. There is minimal pre-processing of the binaries, with features discovery left to the network during training. A crucial difference with other convolutional neural networks (CNN) based approaches is the use of 1-dimensional convolutions; this methodological choice is shown to have significant positive consequences for the detector. In order to compare the detector with state-of-the-art techniques a TF-IDF based benchmark malware detector is also implemented: experiments show an improved accuracy of the proposed CNN detector while maintaining similar training times. The system is also compared, on a publicly available dataset of 11130 binaries, with an existing embedding based CNN detector. The proposed system outperforms, both in accuracy and training time the embedding based CNN.