Abstract
With the frequent use of Wi-Fi and hotspots that provide a wireless Internet environment, awareness and threats to wireless AP security are steadily increasing. Especially when using unauthorized APs in company, government and military facilities, there is a high possibility of being subjected to various viruses and hacking attacks. Therefore, it is necessary to detect and detect authorized APs and unauthorized APs. In this paper, to detect authorized APs and unauthorized APs, the characteristics of RTT (Round Trip Time) values are set as dataset and each machine learning algorithm SVM (Support Vector Machine), J48 (C4.5), KNN (K nearest neighbors), and MLP (Multilayer Perceptron).
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References
Jang, R.-H., et al.: Analysis of time-based unauthorized AP detection methods according to hardware performance of unauthorized AP. J. Korean Inst. Commun. Inf. Sci. 40(3), 551–558 (2015)
Han, H., et al.: A timing-based scheme for rogue AP detection. IEEE Trans. Parallel Distrib. Syst. 22(11), 1912–1925 (2011)
Lee, J., Lee, S., Moon, J.: Detecting rogue AP using k-SVM method. J. Korea Inst. Inf. Secur. Cryptol. 24(1), 87–95 (2014)
Kang, S., Nyang, D., Choi, J., Lee, S.: Relaying rogue AP detection scheme using SVM. J. Korea Inst. Inf. Secur. Cryptol. (JKIISC) 23(3), 431–444 (2013)
Acknowledgments
This work was supported by Defense Acquisition Program Administration and Agency for Defense Development under the contract (UD160066BD).
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Kim, D., Shin, D., Shin, D. (2018). Data Set Construction and Performance Comparison of Machine Learning Algorithm for Detection of Unauthorized AP. In: Park, J., Loia, V., Yi, G., Sung, Y. (eds) Advances in Computer Science and Ubiquitous Computing. CUTE CSA 2017 2017. Lecture Notes in Electrical Engineering, vol 474. Springer, Singapore. https://doi.org/10.1007/978-981-10-7605-3_144
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DOI: https://doi.org/10.1007/978-981-10-7605-3_144
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