Detecting impersonation attack in wifi networks using deep learning approach

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

29 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationInformation Security Applications - 17th International Workshop, WISA 2016, Revised Selected Papers
EditorsDooho Choi, Sylvain Guilley
PublisherSpringer Verlag
Pages136-147
Number of pages12
ISBN (Print)9783319565484
DOIs
Publication statusPublished - 2017
Event17th International Workshop on Information Security Applications, WISA 2016 - Jeju Island, Korea, Republic of
Duration: 25 Aug 201625 Aug 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10144 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference17th International Workshop on Information Security Applications, WISA 2016
Country/TerritoryKorea, Republic of
City Jeju Island
Period25/08/1625/08/16

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