Nowadays, computers and the Internet have become an inseparable part of our life. We accomplish a wide range of our daily tasks through the Internet. A massive number of malwares have been designed annually to infiltrate computers and other electronic devices that endanger their security strikingly. Hence, developing a method that is capable of proactively detect and prevent malware is a perpetual demand. Recently, diverse approaches have been introduced for detecting malware by the help of high-level features and machine learning techniques. Although these methods provide reasonable results, in most of them identifying and extracting proper features from files is one of the most challenging steps. Deep learning techniques that have recently been applied in the area of malware detection, automate the feature extraction operations and represent much better results with respect to multi-layer training. In this study, a novel method is proposed for malware detection by employing a parallel architecture of convolutional neural network (CNN). The proposed method utilises raw bytes of executable files and eliminates the need to extract high-level features. The results of experiments show that the proposed approach can achieve high detection rate, outperforming traditional machine learning based methods which reveals the merit of deep learning techniques in malware detection.

中文翻译：

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并行CNN网络进行恶意软件检测

如今，计算机和互联网已成为我们生活中不可分割的一部分。我们通过Internet完成各种各样的日常任务。每年都设计了大量的恶意软件，以渗透到计算机和其他电子设备中，严重威胁其安全性。因此，迫切需要开发一种能够主动检测和防止恶意软件的方法。最近，已经引入了多种方法来借助高级功能和机器学习技术来检测恶意软件。尽管这些方法提供了合理的结果，但是在大多数方法中，从文件中识别和提取适当的功能是最具挑战性的步骤之一。最近在恶意软件检测领域应用的深度学习技术，使特征提取操作自动化，并在多层训练方面表现出更好的结果。在这项研究中，通过采用卷积神经网络（CNN）的并行体系结构，提出了一种用于恶意软件检测的新方法。所提出的方法利用可执行文件的原始字节，并且不需要提取高级功能。实验结果表明，该方法具有较高的检测率，优于传统的基于机器学习的方法，揭示了深度学习技术在恶意软件检测中的优点。所提出的方法利用可执行文件的原始字节，并且不需要提取高级功能。实验结果表明，该方法具有较高的检测率，优于传统的基于机器学习的方法，揭示了深度学习技术在恶意软件检测中的优点。所提出的方法利用可执行文件的原始字节，并且不需要提取高级功能。实验结果表明，该方法具有较高的检测率，优于传统的基于机器学习的方法，揭示了深度学习技术在恶意软件检测中的优点。