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

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.

[1]  Ibrar Yaqoob,et al.  Big IoT Data Analytics: Architecture, Opportunities, and Open Research Challenges , 2017, IEEE Access.

[2]  John S. Heidemann,et al.  IP-Based IoT Device Detection , 2018, IoT S&P@SIGCOMM.

[3]  Omar Alrawi,et al.  SoK: Security Evaluation of Home-Based IoT Deployments , 2019, 2019 IEEE Symposium on Security and Privacy (SP).

[4]  Dinil Mon Divakaran,et al.  DEFT: A Distributed IoT Fingerprinting Technique , 2019, IEEE Internet of Things Journal.

[5]  Mehmet Hadi Gunes,et al.  Automated IoT Device Identification using Network Traffic , 2019, ICC 2019 - 2019 IEEE International Conference on Communications (ICC).

[6]  Georgios Kambourakis,et al.  DDoS in the IoT: Mirai and Other Botnets , 2017, Computer.

[7]  Soohyung Kim,et al.  Managing IoT devices using blockchain platform , 2017, 2017 19th International Conference on Advanced Communication Technology (ICACT).

[8]  Ahmad-Reza Sadeghi,et al.  IoT SENTINEL: Automated Device-Type Identification for Security Enforcement in IoT , 2016, 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS).

[9]  Jürgen Schmidhuber,et al.  LSTM: A Search Space Odyssey , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[10]  Khaled Salah,et al.  IoT security: Review, blockchain solutions, and open challenges , 2017, Future Gener. Comput. Syst..

[11]  Mohsen Guizani,et al.  Deep Learning for IoT Big Data and Streaming Analytics: A Survey , 2017, IEEE Communications Surveys & Tutorials.

[12]  Ahmad-Reza Sadeghi,et al.  AuDI: Toward Autonomous IoT Device-Type Identification Using Periodic Communication , 2019, IEEE Journal on Selected Areas in Communications.

[13]  Vijay Sivaraman,et al.  Classifying IoT Devices in Smart Environments Using Network Traffic Characteristics , 2019, IEEE Transactions on Mobile Computing.

[14]  Raheem Beyah,et al.  GTID: A Technique for Physical Device and Device Type Fingerprinting , 2015, IEEE Transactions on Dependable and Secure Computing.

[15]  Naphtali Rishe,et al.  IoTSpot: Identifying the IoT Devices Using their Anonymous Network Traffic Data , 2019, MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM).

[16]  Yi Zhou,et al.  Understanding the Mirai Botnet , 2017, USENIX Security Symposium.

[17]  Ming Zhu,et al.  Malware traffic classification using convolutional neural network for representation learning , 2017, 2017 International Conference on Information Networking (ICOIN).

[18]  Nick Feamster,et al.  A Smart Home is No Castle: Privacy Vulnerabilities of Encrypted IoT Traffic , 2017, ArXiv.