IoT Device Classification Methodes Based on Network Traffic

The rapid growth of Internet of Things (IoT) devices in smart cities produces substantial and heterogeneous network traffic, creating significant challenges for security and management. Accurate device classification is crucial for protecting networks against threats, such as malicious devices, and for facilitating efficient resource allocation and management. Although various classification methods have been proposed, a comprehensive review evaluating their effectiveness in addressing the specific challenges of smart cities, such as large-scale device heterogeneity and extensive encrypted traffic, remains lacking.

This paper provides a systematic review of the literature, analyzing and comparing IoT device classification methods that utilize network traffic data. The methodology emphasizes two primary aspects: feature extraction techniques (including packet-level, flow-level, and automated approaches) and the classification algorithms employed (supervised, unsupervised, and deep learning methods).

The analysis shows that packet-based methods achieve high precision but face limitations with encrypted traffic and scalability. In contrast, flow-based and deep learning approaches exhibit greater adaptability. Machine learning (ML) and deep learning (DL) algorithms consistently demonstrate strong performance, with reported accuracy rates reaching up to 99%. The review identifies several critical future research directions for smart city applications, including experimental validation, integration with edge computing, and the development of hybrid classification models.