Intrusion detection for wifi network: A deep learning approach

Shaoqian Wang; Bo Li; Mao Yang; Zhongjiang Yan

doi:10.1007/978-3-030-06158-6_10

Intrusion detection for wifi network: A deep learning approach

Shaoqian Wang
, Bo Li
, Mao Yang
, Zhongjiang Yan

School of Electronics and Information

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

15 Scopus citations

Abstract

With the popularity and development of Wi-Fi network, network security has become a key concern in the recent years. The amount of network attacks and intrusion activities are growing rapidly. Therefore, the continuous improvement of Intrusion Detection Systems (IDS) is necessary. In this paper, we analyse different types of network attacks in wireless networks and utilize Stacked Autoencoder (SAE) and Deep Neural Network (DNN) to perform network attack classification. We evaluate our method on the Aegean WiFi Intrusion Dataset (AWID) and preprocess the dataset by feature selection. In our experiments, we classified the network records into 4 types: normal record, injection attack, impersonation attack and flooding attack. The classification accuracies we achieved of these 4 types of records are 98.4619 $$\%$$, 99.9940 $$\%$$, 98.3936 $$\%$$ and 73.1200 $$\%$$, respectively.

Original language	English
Title of host publication	Wireless Internet - 11th EAI International Conference, WiCON 2018, Proceedings
Editors	Ai-Chun Pang, Der-Jiunn Deng, Chun-Cheng Lin, Jiann-Liang Chen
Publisher	Springer Verlag
Pages	95-104
Number of pages	10
ISBN (Print)	9783030061579
DOIs	https://doi.org/10.1007/978-3-030-06158-6_10
State	Published - 2019
Event	11th International Conference on Wireless Internet , WiCON 2018 - Taipei, Taiwan, Province of China Duration: 15 Oct 2018 → 16 Oct 2018

Publication series

Name	Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Volume	264
ISSN (Print)	1867-8211

Conference

Conference	11th International Conference on Wireless Internet , WiCON 2018
Country/Territory	Taiwan, Province of China
City	Taipei
Period	15/10/18 → 16/10/18

Keywords

Deep learning
Network intrusion detection
WI-FI network

Access to Document

10.1007/978-3-030-06158-6_10

Cite this

Wang, S., Li, B., Yang, M., & Yan, Z. (2019). Intrusion detection for wifi network: A deep learning approach. In A.-C. Pang, D.-J. Deng, C.-C. Lin, & J.-L. Chen (Eds.), Wireless Internet - 11th EAI International Conference, WiCON 2018, Proceedings (pp. 95-104). (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 264). Springer Verlag. https://doi.org/10.1007/978-3-030-06158-6_10

Wang, Shaoqian ; Li, Bo ; Yang, Mao et al. / Intrusion detection for wifi network : A deep learning approach. Wireless Internet - 11th EAI International Conference, WiCON 2018, Proceedings. editor / Ai-Chun Pang ; Der-Jiunn Deng ; Chun-Cheng Lin ; Jiann-Liang Chen. Springer Verlag, 2019. pp. 95-104 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST).

@inproceedings{656db841417b40ad8e53c2b73ba882c5,

title = "Intrusion detection for wifi network: A deep learning approach",

abstract = "With the popularity and development of Wi-Fi network, network security has become a key concern in the recent years. The amount of network attacks and intrusion activities are growing rapidly. Therefore, the continuous improvement of Intrusion Detection Systems (IDS) is necessary. In this paper, we analyse different types of network attacks in wireless networks and utilize Stacked Autoencoder (SAE) and Deep Neural Network (DNN) to perform network attack classification. We evaluate our method on the Aegean WiFi Intrusion Dataset (AWID) and preprocess the dataset by feature selection. In our experiments, we classified the network records into 4 types: normal record, injection attack, impersonation attack and flooding attack. The classification accuracies we achieved of these 4 types of records are 98.4619 \$\$\textbackslash{}\%\$\$, 99.9940 \$\$\textbackslash{}\%\$\$, 98.3936 \$\$\textbackslash{}\%\$\$ and 73.1200 \$\$\textbackslash{}\%\$\$, respectively.",

keywords = "Deep learning, Network intrusion detection, WI-FI network",

author = "Shaoqian Wang and Bo Li and Mao Yang and Zhongjiang Yan",

note = "Publisher Copyright: {\textcopyright} 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.; 11th International Conference on Wireless Internet , WiCON 2018 ; Conference date: 15-10-2018 Through 16-10-2018",

year = "2019",

doi = "10.1007/978-3-030-06158-6\_10",

language = "英语",

isbn = "9783030061579",

series = "Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST",

publisher = "Springer Verlag",

pages = "95--104",

editor = "Ai-Chun Pang and Der-Jiunn Deng and Chun-Cheng Lin and Jiann-Liang Chen",

booktitle = "Wireless Internet - 11th EAI International Conference, WiCON 2018, Proceedings",

}

Wang, S, Li, B , Yang, M & Yan, Z 2019, Intrusion detection for wifi network: A deep learning approach. in A-C Pang, D-J Deng, C-C Lin & J-L Chen (eds), Wireless Internet - 11th EAI International Conference, WiCON 2018, Proceedings. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, vol. 264, Springer Verlag, pp. 95-104, 11th International Conference on Wireless Internet , WiCON 2018, Taipei, Taiwan, Province of China, 15/10/18. https://doi.org/10.1007/978-3-030-06158-6_10

Intrusion detection for wifi network: A deep learning approach. / Wang, Shaoqian; Li, Bo ; Yang, Mao et al.
Wireless Internet - 11th EAI International Conference, WiCON 2018, Proceedings. ed. / Ai-Chun Pang; Der-Jiunn Deng; Chun-Cheng Lin; Jiann-Liang Chen. Springer Verlag, 2019. p. 95-104 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 264).

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

TY - GEN

T1 - Intrusion detection for wifi network

T2 - 11th International Conference on Wireless Internet , WiCON 2018

AU - Wang, Shaoqian

AU - Li, Bo

AU - Yang, Mao

AU - Yan, Zhongjiang

PY - 2019

Y1 - 2019

N2 - With the popularity and development of Wi-Fi network, network security has become a key concern in the recent years. The amount of network attacks and intrusion activities are growing rapidly. Therefore, the continuous improvement of Intrusion Detection Systems (IDS) is necessary. In this paper, we analyse different types of network attacks in wireless networks and utilize Stacked Autoencoder (SAE) and Deep Neural Network (DNN) to perform network attack classification. We evaluate our method on the Aegean WiFi Intrusion Dataset (AWID) and preprocess the dataset by feature selection. In our experiments, we classified the network records into 4 types: normal record, injection attack, impersonation attack and flooding attack. The classification accuracies we achieved of these 4 types of records are 98.4619 $$\%$$, 99.9940 $$\%$$, 98.3936 $$\%$$ and 73.1200 $$\%$$, respectively.

AB - With the popularity and development of Wi-Fi network, network security has become a key concern in the recent years. The amount of network attacks and intrusion activities are growing rapidly. Therefore, the continuous improvement of Intrusion Detection Systems (IDS) is necessary. In this paper, we analyse different types of network attacks in wireless networks and utilize Stacked Autoencoder (SAE) and Deep Neural Network (DNN) to perform network attack classification. We evaluate our method on the Aegean WiFi Intrusion Dataset (AWID) and preprocess the dataset by feature selection. In our experiments, we classified the network records into 4 types: normal record, injection attack, impersonation attack and flooding attack. The classification accuracies we achieved of these 4 types of records are 98.4619 $$\%$$, 99.9940 $$\%$$, 98.3936 $$\%$$ and 73.1200 $$\%$$, respectively.

KW - Deep learning

KW - Network intrusion detection

KW - WI-FI network

UR - https://www.scopus.com/pages/publications/85059964312

U2 - 10.1007/978-3-030-06158-6_10

DO - 10.1007/978-3-030-06158-6_10

M3 - 会议稿件

AN - SCOPUS:85059964312

SN - 9783030061579

T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST

SP - 95

EP - 104

BT - Wireless Internet - 11th EAI International Conference, WiCON 2018, Proceedings

A2 - Pang, Ai-Chun

A2 - Deng, Der-Jiunn

A2 - Lin, Chun-Cheng

A2 - Chen, Jiann-Liang

PB - Springer Verlag

Y2 - 15 October 2018 through 16 October 2018

ER -

Wang S, Li B , Yang M, Yan Z. Intrusion detection for wifi network: A deep learning approach. In Pang AC, Deng DJ, Lin CC, Chen JL, editors, Wireless Internet - 11th EAI International Conference, WiCON 2018, Proceedings. Springer Verlag. 2019. p. 95-104. (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST). doi: 10.1007/978-3-030-06158-6_10

Intrusion detection for wifi network: A deep learning approach

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Keywords

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