Abstract
A tremendous amount of Internet-of-Things (IoT) devices have been deployed in recent years, bringing new challenges for network management and cyber security. It is important for network managers to know what types of IoT devices are connecting to the network. Despite much research efforts, previous works place more emphasis on accuracy but ignore some other performance indicators also in high demand, like efficiency, robustness, adaptability to special scenarios and extensibility for new devices. In this paper, we propose a practical IoT device identification system, namely ByteIoT, based on a simple but well-organized traffic feature, i.e., the frequency distribution of bidirectional packet lengths. ByteIoT applies k-nearest neighbors algorithm as the classifier to gain extensibility and adaptability. We evaluate ByteIoT on several datasets and the results show that ByteIoT can outperform other state-of-the-art methods in the aspects of accuracy, efficiency, extensibility and adaptability.
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