A Review of Computer Vision Methods in Network Security

Abstract

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.

References in corpus (19)

• Prototypical Networks for Few-shot Learning
• Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning
• Recent Advances in Open Set Recognition: A Survey
• Momentum Contrast for Unsupervised Visual Representation Learning
• Simultaneous Feature Learning and Hash Coding with Deep Neural Networks
• Data-Efficient Image Recognition with Contrastive Predictive Coding
• An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks
• Zero-Shot Learning Through Cross-Modal Transfer
• DeepSaliency: Multi-Task Deep Neural Network Model for Salient Object Detection
• Adversarial Attacks and Defences: A Survey
• Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN
• Adversarial Perturbations Against Deep Neural Networks for Malware Classification
• Generating Steganographic Images via Adversarial Training
• Deep learning at the shallow end: Malware classification for non-domain experts
• Imbalanced Malware Images Classification: a CNN based Approach
• Reducing Network Agnostophobia
• Active One-shot Learning
• Adversarial Deep Learning for Robust Detection of Binary Encoded Malware
• Open Dataset of Phishing and Tor Hidden Services Screen-captures