TY  - JOUR
T1  - Application Traffic Classification through Non-Encrypted Data Learning
AU  - Kim, Ju-Sung 
AU  - Jang, Yoon-Seong 
AU  - Baek, Ui-Jun 
AU  - Kim, Myung-Sup 
JO  - The Journal of Korean Institute of Communications and Information Sciences
PY  - 2025
DA  - 2025/1/1
DO  - 10.7840/kics.2025.50.2.304
KW  - Application Traffic
KW  - Traffic Classification
KW  - Deep Learning
AB  - With the growth of the internet, various application traffic types have emerged, and the importance of information security is increasingly emphasized. Consequently, the use of encrypted communication has risen, heightening the need for effective network traffic classification in such environments. However, traditional network traffic classification techniques have shown limitations in handling encrypted data and complex patterns, leading to the emergence of new deep learning-based classification methods. Despite this, deep learning-based classification methods also face challenges in environments with encrypted data or high levels of noise, resulting in lower performance. To address these issues, this paper proposes a methodology that classifies traffic data based on TLS header values to distinguish between encrypted and unencrypted data and utilizes only unencrypted data as training input for the deep learning model. The experiment was conducted using the proposed methodology, resulting in an average accuracy that was 9 percentage points higher than that of the general preprocessing method.