In recent years, network traffic analysis solutions that are driven by artificial intelligence models have achieved impressive performance. The “magic spells” of these solutions come from the knowledge that they learn from large amounts of network traffic data. However, these solutions neglect the impact of the real-world network's complexity on data quality, which makes the knowledge they learn from regular network traffic data difficult to be effective on low-quality data. Considering the packet loss in real-world network environments, this paper presents TFMana to calibrate the inaccurate packet length features extracted from incomplete network traffic data. TFMana utilizes an encoder-based masked language model to predict features of lost packets, incorporating network traffic feature embeddings to enhance prediction accuracy. This approach enables the calibrated features to approximate those extracted from loss-free network traffic asymptotically. Comprehensive experiments are conducted to verify the effectiveness of the proposed method. The evaluation demonstrates that TFMana's calibration achieves recovery accuracy between 83.94 % and 85.66 %, with minimal sensitivity to packet loss rates. Integrated with four benchmark application identification models, TFMana significantly improves classification accuracy under packet loss conditions. Notably, the analysis models maintains reliable performance even at high packet loss rates of 30 %.

Network traffic classification is critical for ensuring network quality, security, and stability. However, the increasing complexity of network environments and the growth of encrypted traffic bring significant challenges. Traditional rule-based, machine learning-based, and deep learning-based approaches are limited by the scarcity of plaintext, reliance on handcrafted features, and the need for large labeled datasets. Pre-training methods have alleviated these issues, but existing models mainly focus on payload semantics and lack dedicated learning of traffic behavior patterns essential for encrypted traffic characterization. Motivated by this, we propose TrafficBM, a dual-modality pre-training framework that jointly models semantic features and traffic behavior patterns. Our approach extracts dualmodality features from network traffic and applies modalityspecific data augmentation to mitigate data imbalance and scarcity. During pre-training, BERT leverages masked bigram modeling (MBM) to capture semantic information, while Mamba uses a masked autoencoder (MAE) architecture to learn traffic behavior patterns. An adaptive gating network, together with a parameter-preserving warm-up strategy, fuses features from both pre-trained models during fine-tuning to improve downstream classification performance. TrafficBM achieves state-of-the-art results on six tasks across eight datasets, including over 0.99 accuracy on five datasets and a 10 % improvement over the best baseline on Datacon2021 Part 2, demonstrating strong generalization and robustness in network traffic classification.

Data traffic has grown exponentially with the rapid development of Narrowband Internet of Things (NB-IoT) technology. Nonorthogonal multiple access (NOMA) and mobile edge computing (MEC) are essential technologies to enhance the performance of NB-IoT networks. This article proposes a new hybrid NOMA offloading strategy, allowing an Internet of Things (IoT) device to execute NOMA with other devices at different periods until the task offloading is completed. An optimization problem is established to minimize the overall offloading delay. To find the solution to the problem, we first use the proposed offloading strategy to determine the pairing method and offloading order of the IoT devices. Then, we transform the delay optimization problem into the link rate maximization problem. Finally, the closed-form solution of the optimal power allocation scheme for each IoT device is derived according to the theoretical analysis and the Karush-Kuhn–Tucker (KKT) condition. The simulation results show that the proposed offloading strategy and power allocation scheme effectively reduce the overall offloading delay under different device numbers and data lengths, which exceeds the orthogonal multiple access (OMA) schemes, the pure NOMA scheme, and the iterative multiuser NOMA scheme in lowering offloading delay.