Abstract
The boost of 5G technology has brought IoT with enhanced feasibility, it is predictable to have a dramatically increased number of the IoT devices with Internet access. However, there will be certain numbers of devices that are extremely vulnerable, particularly with low level of security coding built in. Hackers might have a greater chance to launch DDoS attack. Therefore, identifiable IoT devices linked to the network security vulnerability database might mitigate the risks contributing to a safer cyberspace. This is of great significance to the security of the entire cyberspace. This paper proposes an IoT device identification technology based on network protocol keyword query. Firstly, the traffic packets sent by the IoT device are collected in real time. Then the traffic data carried by different network protocols is extracted by parsing traffic packets. Secondly, the traffic data is filtered and converted into identifiable information related to the identification attribute of the IoT device. Finally, with the search engine of the major Internet of Things e-commerce website, the information about the IoT device is obtained by inputting the protocol keyword.
This work is supported by the NSFC (Grant Nos. 61671087, 61962009, 61572246, 61602232), the Fundamental Research Funds for the Central Universities (Grant No. 2019XD-A02), the Open Foundation of Guizhou Provincial Key Laboratory of Public Big Data (Grant No. 2018BDKFJJ018, 2019BDKFJJ010) and the Scientific Research Foundation of North China University of Technology.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Shi, C.: A novel ensemble learning algorithm based on D-S evidence theory for IoT security. Comput. Mater. Continua 57(3), 635–652 (2018)
Centonze, P.: Security and privacy frameworks for access control big data systems. Comput. Mater. Continua 59(2), 361–374 (2019)
Zou, Y., Liu, S., Yu, N.: IoT Device Recognition Framework based on Web search. J. Cyber Secur. 3(4), 25–40 (2018). (in Chinese)
Cui, A., Stolfo, S.J.: A quantitative analysis of the insecurity of embedded network devices: results of a wide-area scan. In: Proceedings of the 26th Annual Computer Security Applications Conference, pp. 97–106. ACM, December 6, 2010
Radhakrishnan, S.V., Uluagac, A.S., Beyah, R.: GTID: a technique for physical deviceanddevice type fingerprinting. IEEE Trans. Depend. Secure Comput. 12(5), 519–32 (2014)
Cao, L.C., Zhao, J.J., Cui, X., Li, K.: Cyberspace device identification based on K-means with cosine distance measure. J. Univ. Chin. Acad. Sci. 33(4), 562–569 (2016)
Ren, R.L., Gu, Y., Cui, J., Liu, S., Zhu, H.S., Sun, L.M.: Web features-based recognition specific-type IoT device in cyber-space. Commun. Technol. 50(5), 1003–1009 (2017). (in Chinese)
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, June 5, 2017
Li, Q., Feng, X., Wang, H., Sun, L.: Automatically discovering surveillance devices in the cyberspace. In: Proceedings of the 8th ACM on Multimedia Systems Conference, pp. 331–342. ACM, June 20, 2017
Meidan, Y., et al.: ProfilIoT: a machine learning approach for IoT device identification based on network traffic analysis. In: Proceedings of the Symposium on Applied Computing, pp. 506–509. ACM, April 3, 2017
Tu, Y., Zhang, Z., Li, Y., Wang, C., Xiao, Y.: Research on the internet of things device recognition based on RF-fingerprinting. IEEE Access. 22(7), 37426–31 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Xu, ZX., Dai, Qy., Xu, G., Huang, H., Chen, XB., Yang, YX. (2020). IoT Device Recognition Framework Based on Network Protocol Keyword Query. In: Sun, X., Wang, J., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2020. Lecture Notes in Computer Science(), vol 12239. Springer, Cham. https://doi.org/10.1007/978-3-030-57884-8_20
Download citation
DOI: https://doi.org/10.1007/978-3-030-57884-8_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-57883-1
Online ISBN: 978-3-030-57884-8
eBook Packages: Computer ScienceComputer Science (R0)