IoT ETEI: End-to-End IoT Device Identification Method

Feihong Yin, Li Yang, Yuchen Wang, Jiahao Dai

202137 citationsDOI

Abstract

The past decades have seen the rapid development of Internet of Things (IoT) in various domains. Identifying the IoT devices connected to the network is a crucial aspect of network security. However, existing work on identifying IoT devices based on manually extracted features and prior knowledge, leading to low efficiency and identification accuracy. In this paper, we propose an automatic end-to-end IoT device identification method (IoT ETEI) based on CNN+BiLSTM deep learning model, which outperforms traditional methods from the perspective of overhead and identify accuracy. We demonstrate the effectiveness and flexibility of the proposed method by deploying IoT ETEI in the face of identifying IoT devices on public datasets with the accuracy rate over 99 %, even for IoT devices that use encryption protocols.

Topics & Concepts

Computer scienceInternet of ThingsIdentification (biology)Flexibility (engineering)Overhead (engineering)End-to-end principleEncryptionEmbedded systemArtificial intelligenceComputer networkBiologyMathematicsOperating systemStatisticsBotanyInternet Traffic Analysis and Secure E-votingNetwork Security and Intrusion DetectionAdvanced Malware Detection Techniques