IoTClassifier

This repository contains the re-implementations of several IoT device classification systems, also known as passive device fingerprinting.

The implemented algorithms process and analyze traffic data in an offline manner. TShark is used to extract original traffic features and export them as CSV format.

Paper References

• [1] Marchal, Samuel, et al. "Audi: Toward autonomous iot device-type identification using periodic communication." IEEE Journal on Selected Areas in Communications 37.6 (2019): 1402-1412.
• [2] Shahid, Mustafizur R., et al. "IoT devices recognition through network traffic analysis." 2018 IEEE international conference on big data (big data). IEEE, 2018.
• [3] Bezawada, B., Bachani, M., Peterson, J., Shirazi, H., Ray, I., & Ray, I. (2018). "Iotsense: Behavioral fingerprinting of iot devices". arXiv preprint arXiv:1804.03852.
• [4] Sivanathan, Arunan, et al. "Classifying IoT devices in smart environments using network traffic characteristics." IEEE Transactions on Mobile Computing 18.8 (2018): 1745-1759.
• [5] Chenxin D., Hao G., Guanglei S., Jiahai Y. and Zhiliang W. "ByteIoT: a Practical IoT Device Identification System based on Packet Length Distribution", in IEEE Transactions on Network and Service Management, 2021. doi: 10.1109/TNSM.2021.3130312.

Other Related Works

• [6] Meidan, Yair, et al. "ProfilIoT: A machine learning approach for IoT device identification based on network traffic analysis." Proceedings of the symposium on applied computing. 2017.
• [7] Miettinen, Markus, et al. "Iot sentinel: Automated device-type identification for security enforcement in iot." 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS). IEEE, 2017.
• [8] Meidan Y, Bohadana M, Shabtai A, et al. Detection of unauthorized IoT devices using machine learning techniques[J]. arXiv preprint arXiv:1709.04647, 2017.
• [9] Lopez-Martin, Manuel, et al. "Network traffic classifier with convolutional and recurrent neural networks for Internet of Things." IEEE Access 5 (2017): 18042-18050.
• [10] J. Ortiz, C. Crawford, and F. Le, “Devicemien: Network device behavior modeling for identifying unknown iot devices,” in Proceedings of the International Conference on Internet of Things Design and Implementation, ser. IoTDI’19.New York, NY, USA: ACM, 2019, pp. 106–117.
• [11] BremlerBarr, Anat, Haim Levy, and Zohar Yakhini. "IoT or NoT: Identifying IoT Devices in a Short Time Scale." NOMS 2020-2020 IEEE/IFIP Network Operations and Management Symposium. IEEE, 2020.
• [12] Kolcun, Roman, et al. "The case for retraining of ML models for IoT device identification at the edge." arXiv preprint arXiv:2011.08605 (2020).
• [13] Pinheiro, Antônio J., et al. "Identifying IoT devices and events based on packet length from encrypted traffic." Computer Communications 144 (2019): 8-17.

Open Datasets

There are some open datasets which collect traffic traces generated by different IoT devices and can be used to validate and reproduce the results presented in the above papers.

• UNSW Dataset: https://iotanalytics.unsw.edu.au/iottraces.html
• YourThings Dataset: https://yourthings.info/data/