Network Traffic Detection in Software‐Defined Network Using Optimized Rotation‐Invariant Coordinate Convolutional Neural Network
Visramsetty Sujatha, S. Prabakeran · International Journal of Communication Systems · 2025
ABSTRACT software‐defined networking (SDN) offers flexible traffic management but remains vulnerable to sophisticated cyberattacks, necessitating accurate and efficient network traffic detection. Existing SDN‐based intrusion detection systems often suffer from high computational cost, poor scalability, and reduced accuracy in high‐throughput or encrypted environments. To address these limitations, the NTD‐SDN‐RICCNN framework is proposed, which integrates fast robust iterative filtering (FRIF) for noise removal with spectral graph fast Fourier transform (SGFFT) for discriminative feature extraction. Rotation‐invariant coordinate convolutional neural network (RICCNN) optimized with weighted velocity‐guided gray wolf optimizer (WVGWO) for parameter tuning. The proposed method reduces redundant feature processing while improving detection accuracy and inference speed. Experiments on the SDN intrusion detection dataset show that NTD‐SDN‐RICCNN attains 99.7% accuracy, 99.6% precision, 99.5% recall, and reduces computational time by up to 32.5% compared to the state‐of‐the‐art baselines. These results demonstrate the method's effectiveness and scalability for real‐time SDN intrusion detection in diverse network conditions.