Network security has become an area of significant importance more than ever as highlighted by the eye-opening numbers of data breaches, attacks on critical infrastructure, and malware/ransomware/cryptojacker attacks that are reported almost every day. Increasingly, we are relying on networked infrastructure and with the advent of IoT, billions of devices will be connected to the Internet, providing attackers with more opportunities to exploit. Traditional machine learning methods have been frequently used in the context of network security. However, such methods are more based on statistical features extracted from sources such as binaries, emails, and packet flows. On the other hand, recent years witnessed a phenomenal growth in computer vision mainly driven by the advances in the area of convolutional neural networks. At a glance, it is not trivial to see how computer vision methods are related to network security. Nonetheless, there is a significant amount of work that highlighted how methods from computer vision can be applied in network security for detecting attacks or building security solutions. In this paper, we provide a comprehensive survey of such work under three topics; i) phishing attempt detection, ii) malware detection, and iii) traffic anomaly detection. We also discuss existing research gaps and future research directions, especially focusing on how network security research community and the industry can leverage the exponential growth of computer vision methods to build much secure networked systems. Finally, we review a set of such commercial products for which public information is available and explore how computer vision methods are effectively used in those products and conclude with a brief overview of commonly used computer vision methods in this domain.

中文翻译：

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网络安全中的计算机视觉方法综述


网络安全已成为一个比以往任何时候都更加重要的领域，几乎每天都会报告数量惊人的数据泄露、对关键基础设施的攻击以及恶意软件/勒索软件/加密劫持者攻击。我们越来越依赖网络基础设施，随着物联网的出现，数十亿设备将连接到互联网，为攻击者提供更多的利用机会。传统的机器学习方法已频繁应用于网络安全领域。然而，此类方法更多地基于从二进制文件、电子邮件和数据包流等源中提取的统计特征。另一方面，近年来计算机视觉领域出现了显着增长，这主要是由卷积神经网络领域的进步推动的。乍一看，计算机视觉方法与网络安全的关系并不简单。尽管如此，仍有大量工作强调了如何将计算机视觉方法应用于网络安全以检测攻击或构建安全解决方案。在本文中，我们在三个主题下对此类工作进行了全面的调查； i) 网络钓鱼尝试检测，ii) 恶意软件检测，以及 iii) 流量异常检测。我们还讨论了现有的研究差距和未来的研究方向，特别关注网络安全研究社区和行业如何利用计算机视觉方法的指数增长来构建更加安全的网络系统。 最后，我们回顾了一组可以获得公开信息的此类商业产品，并探讨了如何在这些产品中有效地使用计算机视觉方法，并简要概述了该领域常用的计算机视觉方法。