Implement Detecting Network Attack through Machine Learning in LoRaWAN Environment 


Vol. 44,  No. 8, pp. 1547-1555, Aug.  2019
10.7840/kics.2019.44.8.1547


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  Abstract

This paper analyzes the type and mechanism of network security threats arising in LoRaWAN (Long Range Wide Area Network), a low-power and long-range communication support technology that is drawing attention as the era of the Internet of Things (IoT) arrives, and proposes a method to detect these threats in advance by applying machine learning. LoRaWAN"s unique architecture makes it difficult to apply security detection algorithms that were generally used in IPS/IDS. Therefore, the proposed algorithm presents a new approach through clustering of machine learning techniques for messages occurring in LoRaWAN environments, and performs very excellent performance in detection of attacks such as message reuse attack and spoofing. In fact, the 100% accuracy of detection and the low time required, which have been verified in several experiments suggest great potential in applying it to the actual LoRaWAN environment.

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  Cite this article

[IEEE Style]

T. Jung, S. Lee, K. Kim, "Implement Detecting Network Attack through Machine Learning in LoRaWAN Environment," The Journal of Korean Institute of Communications and Information Sciences, vol. 44, no. 8, pp. 1547-1555, 2019. DOI: 10.7840/kics.2019.44.8.1547.

[ACM Style]

Tack-hyun Jung, Seung-ho Lee, and Kee-cheon Kim. 2019. Implement Detecting Network Attack through Machine Learning in LoRaWAN Environment. The Journal of Korean Institute of Communications and Information Sciences, 44, 8, (2019), 1547-1555. DOI: 10.7840/kics.2019.44.8.1547.

[KICS Style]

Tack-hyun Jung, Seung-ho Lee, Kee-cheon Kim, "Implement Detecting Network Attack through Machine Learning in LoRaWAN Environment," The Journal of Korean Institute of Communications and Information Sciences, vol. 44, no. 8, pp. 1547-1555, 8. 2019. (https://doi.org/10.7840/kics.2019.44.8.1547)

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pISSN : 1226 - 4717
eISSN : 2287 - 3880