Deep Learning Approaches for Network Traffic Classification in the Internet of Things (IoT): A Survey
It addresses the problem of optimizing resource allocation and security in IoT systems, but it is incremental as it synthesizes existing research rather than presenting new methods.
This survey paper reviews deep learning approaches for classifying network traffic in IoT environments, analyzing their strengths and limitations to provide insights and identify research gaps for future work.
The Internet of Things (IoT) has witnessed unprecedented growth, resulting in a massive influx of diverse network traffic from interconnected devices. Effectively classifying this network traffic is crucial for optimizing resource allocation, enhancing security measures, and ensuring efficient network management in IoT systems. Deep learning has emerged as a powerful technique for network traffic classification due to its ability to automatically learn complex patterns and representations from raw data. This survey paper aims to provide a comprehensive overview of the existing deep learning approaches employed in network traffic classification specifically tailored for IoT environments. By systematically analyzing and categorizing the latest research contributions in this domain, we explore the strengths and limitations of various deep learning models in handling the unique challenges posed by IoT network traffic. Through this survey, we aim to offer researchers and practitioners valuable insights, identify research gaps, and provide directions for future research to further enhance the effectiveness and efficiency of deep learning-based network traffic classification in IoT.