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This work provides a comparative analysis illustrating how Deep Learning (DL)surpasses Machine Learning (ML) in addressing tasks within Internet of Things(IoT), such as attack classification and device-type identification. Ourapproach involves training and evaluating a DL model using a range of diverseIoT-related datasets, allowing us to gain valuable insights into how adaptableand practical these models can be when confronted with various IoTconfigurations. We initially convert the unstructured network traffic data fromIoT networks, stored in PCAP files, into images by processing the packet data.This conversion process adapts the data to meet the criteria of DLclassification methods. The experiments showcase the ability of DL to surpassthe constraints tied to manually engineered features, achieving superiorresults in attack detection and maintaining comparable outcomes in device-typeidentification. Additionally, a notable feature extraction time differencebecomes evident in the experiments: traditional methods require around 29milliseconds per data packet, while DL accomplishes the same task in just 2.9milliseconds. The significant time gap, DL's superior performance, and therecognized limitations of manually engineered features, presents a compellingcall to action within the IoT community. This encourages us to shift fromexploring new IoT features for each dataset to addressing the challenges ofintegrating DL into IoT, making it a more efficient solution for real-world IoTscenarios.

Enhancing IoT Security via Automatic Network Traffic Analysis: The Transition from Machine Learning to Deep Learning