Time-Distributed Feature Learning for Internet of Things Network Traffic Classification
<|reference_start|>Time-Distributed Feature Learning for Internet of Things Network Traffic Classification: Deep learning-based network traffic classification (NTC) techniques, including conventional and class-of-service (CoS) classifiers, are a popular tool that aids in the quality of service (QoS) and radio resource management for the Internet of Things (IoT) network. Holistic temporal features consist of inter-, intra-, and pseudo-temporal features within packets, between packets, and among flows, providing the maximum information on network services without depending on defined classes in a problem. Conventional spatio-temporal features in the current solutions extract only space and time information between packets and flows, ignoring the information within packets and flow for IoT traffic. Therefore, we propose a new, efficient, holistic feature extraction method for deep-learning-based NTC using time-distributed feature learning to maximize the accuracy of the NTC. We apply a time-distributed wrapper on deep-learning layers to help extract pseudo-temporal features and spatio-temporal features. Pseudo-temporal features are mathematically complex to explain since, in deep learning, a black box extracts them. However, the features are temporal because of the time-distributed wrapper; therefore, we call them pseudo-temporal features. Since our method is efficient in learning holistic-temporal features, we can extend our method to both conventional and CoS NTC. Our solution proves that pseudo-temporal and spatial-temporal features can significantly improve the robustness and performance of any NTC. We analyze the solution theoretically and experimentally on different real-world datasets. The experimental results show that the holistic-temporal time-distributed feature learning method, on average, is 13.5% more accurate than the state-of-the-art conventional and CoS classifiers.<|reference_end|>
@article{manjunath2024time-distributed,
    title={Time-Distributed Feature Learning for Internet of Things Network Traffic
  Classification},
    author={Yoga Suhas Kuruba Manjunath, Sihao Zhao, Xiao-Ping Zhang, Lian Zhao},
    journal={arXiv preprint arXiv:2409.05096},
    year={2024},
    doi={10.1109/TNSM.2024.3457579},
    archivePrefix={arXiv},
    eprint={2409.05096},
    primaryClass={eess.SP cs.LG}
}