  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.
Next, 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. Finally, we 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.
