WITHDRAWN DNS over HTTPS (DoH) Traffic Flows Detection Using One-dimensional Convolution Neural Networks.

As internet connectivity rapidly advances, safeguarding user privacy has become paramount. DNS over HTTPS (DoH), a novel technology, was created to enhance internet users' privacy protection. DoH encapsulates queries and responses within Hypertext Transfer Protocol Secure (HTTPS) and can replace traditional DNS for domain name resolution. While DoH offers benefits, it also presents challenges. Although it improves user privacy, its encapsulation mechanism complicates detection for enterprises employing conventional methods to monitor network activity and potential threats. This study explores features that effectively represent DoH traffic classification, as these features directly impact the model's classification accuracy. We utilized the publicly available CIRA-CIC-DoHBrw2020 dataset for comparative analysis and experimentation. To determine feature importance, we categorized the dataset's features into two types: those with and without network-specific characteristics. We then developed a One-dimensional Convolutional Neural Network model based on features that accurately represent DoH traffic. The One-dimensional Convolutional Neural Network model, built on the classified features, distinguishes DoH traffic from other network traffic with enhanced precision. We evaluated the proposed method's performance using accuracy metrics, achieving a score of 99.06 accuracy.

Encrypted traffic classification, DNS over HTTPS,

Feature representation, 1D-CNN 

Data Visualization

Symposium on Data Science and Statistics (SDSS) 2025