Abstract
WiFi network traffics will be expected to increase sharply in the coming years, since WiFi network is commonly used for local area connectivity. Unfortunately, there are difficulties in WiFi network research beforehand, since there is no common dataset between researchers on this area. Recently, AWID dataset was published as a comprehensive WiFi network dataset, which derived from real WiFi traces. The previous work on this AWID dataset was unable to classify Impersonation Attack sufficiently. Hence, we focus on optimizing the Impersonation Attack detection. Feature selection can overcome this problem by selecting the most important features for detecting an arbitrary class. We leverage Artificial Neural Network (ANN) for the feature selection and apply Stacked Auto Encoder (SAE), a deep learning algorithm as a classifier for AWID Dataset. Our experiments show that the reduced input features have significantly improved to detect the Impersonation Attack.
This work was partly supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (B0101-16-1270, Research on Communication Technology using Bio-Inspired Algorithm) and the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. NRF-2015R1A2A2A01006812).
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The authors are very grateful to anonymous reviewers for their valuable feedbacks and suggestions.
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Aminanto, M.E., Kim, K. (2017). Detecting Impersonation Attack in WiFi Networks Using Deep Learning Approach. In: Choi, D., Guilley, S. (eds) Information Security Applications. WISA 2016. Lecture Notes in Computer Science(), vol 10144. Springer, Cham. https://doi.org/10.1007/978-3-319-56549-1_12
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DOI: https://doi.org/10.1007/978-3-319-56549-1_12
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