Deep Learning Approaches for Network Traffic Classification in the
  Internet of Things (IoT): A Survey
Jawad Hussain Kalwar Sania Bhatti
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.