TY - GEN
T1 - Detecting impersonation attack in wifi networks using deep learning approach
AU - Aminanto, Muhamad Erza
AU - Kim, Kwangjo
N1 - Funding Information:
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). The authors are very grateful to anonymous reviewers for their valuable feedbacks and suggestions.
Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85017641050&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-56549-1_12
DO - 10.1007/978-3-319-56549-1_12
M3 - Conference contribution
AN - SCOPUS:85017641050
SN - 9783319565484
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 136
EP - 147
BT - Information Security Applications - 17th International Workshop, WISA 2016, Revised Selected Papers
A2 - Choi, Dooho
A2 - Guilley , Sylvain
PB - Springer Verlag
T2 - 17th International Workshop on Information Security Applications, WISA 2016
Y2 - 25 August 2016 through 25 August 2016
ER -