TY  - JOUR
T1  - A Survey on Deep Learning-Based Image Approaches for Malicious Network Traffic Detection
AU  - Park, Young-joo 
AU  - Lee, Sun Woo 
JO  - The Journal of Korean Institute of Communications and Information Sciences
PY  - 2025
DA  - 2025/1/1
DO  - 10.7840/kics.2025.50.11.1739
KW  - Malware Traffic Detection
KW  - Malicious Traffic Detection
KW  - Image-Based Classification
AB  - As cyberattacks grow more sophisticated, detecting malicious behavior through network traffic has become increasingly critical. Recently, image-based detection approaches that convert raw network traffic into grayscale or RGB images for deep learning classification have gained attention. This paper provides a structured survey on such methods, categorizing existing research by model architecture (e.g., CNN, CNN-LSTM, GAN, GNN), image processing techniques, target environments such as IoT, and evaluation settings. We also examine open-set recognition (OSR) approaches aimed at identifying previously unseen attacks. By analyzing preprocessing strategies, image encoding methods, and OSR applications, this study outlines key limitations of current research and discusses future directions, including lightweight modeling for IoT, real-time performance, protocol independence, and enhanced OSR techniques.