LRT-DDPM: A Diffusion Model-Based Approach for Network Traffic Data Generation in Intrusion Detection

Network Intrusion Detection Systems (NIDS) are fundamental to network security. However, traditional approaches often struggle with high-dimensional and imbalanced network traffic data. To address these challenges, we propose LRT-DDPM, a novel network traffic data generation model based on the Denoising Diffusion Probabilistic Model (DDPM). Our model employs a stepwise denoising mechanism to generate high-fidelity synthetic data, enhances feature extraction through a convolutional neural network with residual connections and introduces the Time-Adaptive Control Block (TACBlock) strategy for dynamic time step adjustment during diffusion. LRT-DDPM demonstrates superior performance on two widely-used benchmark datasets. It achieves an accuracy of 99.40% on NSL-KDD and 99.53% on CICIDS2017. Furthermore, the model attains high precision, recall, and F1-score metrics, consistently outperforms existing methods by enhancing detection accuracy and reducing false positive rates.