Abstract
The Internet of Things (IoT) has developed rapidly in recent years and has been widely used in our daily life. An online report claimed that the connected IoT devices will reach the scale of 14.4 billion globally at the end of 2022. With the rapid and large-scale deployment of such devices, however, some severe security problems and challenges arised as well, especially in the field of IoT device management. Device identification is a prerequisite procedure to mitigate the above issues. Therefore, accurately identifying the deployed IoT devices plays a vital role in network management and cyber security. In this work, we come up with a spatio-temporal-based method that characterizes IoT device behaviors by leveraging the packet sequence features of IoT traffic, which is able to automatically extract the high-level features from raw IoT traffic. The further evaluation indicates that our method is capable of identifying diverse IoT devices with satisfactory accuracy.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Number of connected IoT devices growing 18% to 14.4 billion globally. https://iot-analytics.com/number-connected-iot-devices/
RFC 793 - transmission control protocol. https://datatracker.ietf.org/doc/html/rfc793
Antonakakis, M., et al.: Understanding the mirai botnet. In: 26th USENIX Security Symposium (USENIX Security 2017), pp. 1093–1110 (2017)
Dadkhah, S., Mahdikhani, H., Danso, P.K., Zohourian, A., Truong, K.A., Ghorbani, A.A.: Towards the development of a realistic multidimensional IoT profiling dataset. In: 2022 19th Annual International Conference on Privacy, Security & Trust (PST), pp. 1–11. IEEE (2022)
Dainotti, A., Pescape, A., Claffy, K.C.: Issues and future directions in traffic classification. IEEE Network 26(1), 35–40 (2012)
Duan, C., Gao, H., Song, G., Yang, J., Wang, Z.: ByteIoT: a practical IoT device identification system based on packet length distribution. IEEE Trans. Netw. Service Manag. (2021)
Feng, X., Li, Q., Wang, H., Sun, L.: Acquisitional rule-based engine for discovering internet-of-thing devices. In: Proceedings of the 27th USENIX Conference on Security Symposium, pp. 327–341 (2018)
Garcia, S., Parmisano, A., Erquiaga, M.J.: IoT-23: a labeled dataset with malicious and benign IoT network traffic. Stratosphere Lab., Praha, Czech Republic, Technical report (2020)
Hamad, S.A., Zhang, W.E., Sheng, Q.Z., Nepal, S.: IoT device identification via network-flow based fingerprinting and learning. In: 2019 18th IEEE International Conference on Trust, Security and Privacy in Computing and Communications/13th IEEE International Conference on Big Data Science and Engineering (TrustCom/BigDataSE), pp. 103–111. IEEE (2019)
Hamza, A., Gharakheili, H.H., Benson, T.A., Sivaraman, V.: Detecting volumetric attacks on lot devices via SDN-based monitoring of mud activity. In: Proceedings of the 2019 ACM Symposium on SDN Research, pp. 36–48 (2019)
Li, J., Li, Z., Tyson, G., Xie, G.: Your privilege gives your privacy away: an analysis of a home security camera service. In: IEEE INFOCOM 2020-IEEE Conference on Computer Communications, pp. 387–396. IEEE (2020)
Loi, F., Sivanathan, A., Gharakheili, H.H., Radford, A., Sivaraman, V.: Systematically evaluating security and privacy for consumer IoT devices. In: Proceedings of the 2017 Workshop on Internet of Things Security and Privacy, pp. 1–6 (2017)
Ma, X., Qu, J., Li, J., Lui, J.C., Li, Z., Guan, X.: Pinpointing hidden IoT devices via spatial-temporal traffic fingerprinting. In: IEEE INFOCOM 2020-IEEE Conference on Computer Communications, pp. 894–903. IEEE (2020)
Marchal, S., Miettinen, M., Nguyen, T.D., Sadeghi, A.R., Asokan, N.: AuDI: toward autonomous IoT device-type identification using periodic communication. IEEE J. Sel. Areas Commun. 37(6), 1402–1412 (2019)
Miettinen, M., Marchal, S., Hafeez, I., Asokan, N., Sadeghi, A.R., Tarkoma, S.: IoT sentinel: automated device-type identification for security enforcement in IoT. In: 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS), pp. 2177–2184. IEEE (2017)
Nguyen, T.D., Marchal, S., Miettinen, M., Fereidooni, H., Asokan, N., Sadeghi, A.R.: Dïot: a federated self-learning anomaly detection system for IoT. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 756–767. IEEE (2019)
Notra, S., Siddiqi, M., Gharakheili, H.H., Sivaraman, V., Boreli, R.: An experimental study of security and privacy risks with emerging household appliances. In: 2014 IEEE Conference on Communications and Network Security, pp. 79–84. IEEE (2014)
Pinheiro, A.J., Bezerra, J.D.M., Burgardt, C.A., Campelo, D.R.: Identifying IoT devices and events based on packet length from encrypted traffic. Comput. Commun. 144, 8–17 (2019)
Ren, J., Dubois, D.J., Choffnes, D., Mandalari, A.M., Kolcun, R., Haddadi, H.: Information exposure from consumer IoT devices: a multidimensional, network-informed measurement approach. In: Proceedings of the Internet Measurement Conference, pp. 267–279 (2019)
Sadeghi, A.R., Wachsmann, C., Waidner, M.: Security and privacy challenges in industrial internet of things. In: 2015 52nd ACM/EDAC/IEEE Design Automation Conference (DAC), pp. 1–6. IEEE (2015)
Sivanathan, A., et al.: Classifying IoT devices in smart environments using network traffic characteristics. IEEE Trans. Mob. Comput. 18(8), 1745–1759 (2018)
Thangavelu, V., Divakaran, D.M., Sairam, R., Bhunia, S.S., Gurusamy, M.: Deft: a distributed IoT fingerprinting technique. IEEE Internet Things J. 6(1), 940–952 (2018)
Trimananda, R., Varmarken, J., Markopoulou, A., Demsky, B.: Packet-level signatures for smart home devices. In: Network and Distributed Systems Security (NDSS) Symposium, vol. 2020 (2020)
Yang, K., Li, Q., Sun, L.: Towards automatic fingerprinting of IoT devices in the cyberspace. Comput. Netw. 148, 318–327 (2019)
Yin, F., Yang, L., Wang, Y., Dai, J.: IoT ETEI: end-to-end IoT device identification method. In: 2021 IEEE Conference on Dependable and Secure Computing (DSC), pp. 1–8. IEEE (2021)
Acknowledgements
We thank the anonymous reviewers for their insightful comments. This work is supported by The National Key Research and Development Program of China under Grant No. 2019YFB1005201, No. 2019YFB1005203 and No. 2019YFB1005205.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Ma, J., Sang, Y., Zhang, Y., Xu, X., Feng, B., Zeng, Y. (2022). An Adaptive Ensembled Neural Network-Based Approach to IoT Device Identification. In: Gao, H., Wang, X., Wei, W., Dagiuklas, T. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 461. Springer, Cham. https://doi.org/10.1007/978-3-031-24386-8_12
Download citation
DOI: https://doi.org/10.1007/978-3-031-24386-8_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-24385-1
Online ISBN: 978-3-031-24386-8
eBook Packages: Computer ScienceComputer Science (R0)