Optimizing Network Traffic Classification Models with a Hybrid Approach for Large-Scale Data - Handoko, Andrew C; Hendry, Hendry; Wellem, Theophilus (Fakultas Ilmu Komputer, Universitas Klabat, 2025)
The escalating threat of cyberattacks necessitates the development of intrusion detection models that are both accurate and computationally efficient for large-scale network traffic. To address this issue, this study proposes a hybrid approach combining Autoencoder, Convolutional Neural Network (CNN), and XGBoost as an adaptive and lightweight solution for network traffic classification. The key contribution of this research lies in the design of a multi-stage pipeline that performs dimensionality reduction, feature extraction, and final classification. The model was evaluated using the Moore Dataset, which contains complex and high-dimensional network traffic data. The experimental results indicate that the proposed hybrid model achieved a classification accuracy of 99.20% with a testing time of only 0.09 seconds. Furthermore, the pipeline significantly reduced computational load compared to single CNN or XGBoost models. These findings demonstrate that the hybrid approach not only offers high classification performance but also enhances scalability and efficiency, making it suitable for real-world implementation in modern network security systems. Overall, the proposed model presents a promising and practical solution for advancing future intrusion detection systems.