Detecting impersonation attack in wifi networks using deep learning approach

Muhamad Erza Aminanto; Kwangjo Kim

doi:10.1007/978-3-319-56549-1_12

Detecting impersonation attack in wifi networks using deep learning approach

Muhamad Erza Aminanto, Kwangjo Kim

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

38 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 language	English
Title of host publication	Information Security Applications - 17th International Workshop, WISA 2016, Revised Selected Papers
Editors	Dooho Choi, Sylvain Guilley
Publisher	Springer
Pages	136-147
Number of pages	12
ISBN (Print)	9783319565484
DOIs	https://doi.org/10.1007/978-3-319-56549-1_12
Publication status	Published - 2017
Externally published	Yes
Event	International Workshop on Information Security Applications 2016 - Jeju Island, Korea, South Duration: 25 Aug 2016 → 25 Aug 2016 Conference number: 17th https://link.springer.com/book/10.1007/978-3-319-56549-1

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	10144
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	International Workshop on Information Security Applications 2016
Abbreviated title	WISA 2016
Country/Territory	Korea, South
City	Jeju Island
Period	25/08/16 → 25/08/16
Internet address	https://link.springer.com/book/10.1007/978-3-319-56549-1

Access to Document

10.1007/978-3-319-56549-1_12

Cite this

@inproceedings{db73e58e59ec4919b9dd207654ed6a97,

title = "Detecting impersonation attack in wifi networks using deep learning approach",

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.",

author = "Aminanto, {Muhamad Erza} and Kwangjo Kim",

note = "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: {\textcopyright} Springer International Publishing AG 2017.; International Workshop on Information Security Applications 2016, WISA 2016 ; Conference date: 25-08-2016 Through 25-08-2016",

year = "2017",

doi = "10.1007/978-3-319-56549-1_12",

language = "English",

isbn = "9783319565484",

series = "Lecture Notes in Computer Science",

publisher = "Springer",

pages = "136--147",

editor = "Dooho Choi and {Guilley }, Sylvain",

booktitle = "Information Security Applications - 17th International Workshop, WISA 2016, Revised Selected Papers",

url = "https://link.springer.com/book/10.1007/978-3-319-56549-1",

}

Aminanto, ME & Kim, K 2017, Detecting impersonation attack in wifi networks using deep learning approach. in D Choi & S Guilley (eds), Information Security Applications - 17th International Workshop, WISA 2016, Revised Selected Papers. Lecture Notes in Computer Science, vol. 10144, Springer, pp. 136-147, International Workshop on Information Security Applications 2016, Jeju Island, Korea, South, 25/08/16. https://doi.org/10.1007/978-3-319-56549-1_12

Detecting impersonation attack in wifi networks using deep learning approach. / Aminanto, Muhamad Erza; Kim, Kwangjo.
Information Security Applications - 17th International Workshop, WISA 2016, Revised Selected Papers. ed. / Dooho Choi; Sylvain Guilley . Springer, 2017. p. 136-147 (Lecture Notes in Computer Science; Vol. 10144).

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

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Detecting impersonation attack in wifi networks using deep learning approach

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