Deep learning approach for ids using dnn for network anomaly detection

Zhiqiang Liu; Mohi Ud Din Ghulam; Ye Zhu; Xuanlin Yan; Lifang Wang; Zejun Jiang; Jianchao Luo

doi:10.1007/978-981-15-0637-6_40

Deep learning approach for ids using dnn for network anomaly detection

Zhiqiang Liu, Mohi Ud Din Ghulam, Ye Zhu, Xuanlin Yan, Lifang Wang, Zejun Jiang, Jianchao Luo

School of Cybersecurity

Northwestern Polytechnical University Xian

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

12 Scopus citations

Abstract

With the astonishing development of the Internet and its applications in the last decade, cyberattacks are changing quickly, and the necessity of protection for communication network has improved tremendously. As the primary defense, the intrusion detection system plays a crucial role in making sure the network security. Key to intrusion detection system is actually to determine a variety of attacks effectively as well as to adjust to a constantly changing threat scenario. DNN or Deep Neural Network on NSL-KDD dataset for effective detection of an attack. Firstly, the dataset was preprocessed and normalized and then fed to the DNN algorithm to create a model. For testing purpose, entire dataset of NSL-KDD was used. Finally, to analyze the accuracy and precision of the DNN model, we use accuracy and precision matrices. The proposed DNN-based strategy enhances network anomaly detection and opens new analysis gateway for intrusion detection systems.

Original language	English
Title of host publication	4th International Congress on Information and Communication Technology, ICICT 2019, Volume 1
Editors	Xin-She Yang, Simon Sherratt, Nilanjan Dey, Amit Joshi
Publisher	Springer
Pages	471-479
Number of pages	9
ISBN (Print)	9789811506369
DOIs	https://doi.org/10.1007/978-981-15-0637-6_40
State	Published - 2020
Event	4th International Congress on Information and Communication Technology, ICICT 2019 - London, United Kingdom Duration: 27 Feb 2019 → 28 Feb 2019

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	1041
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Conference

Conference	4th International Congress on Information and Communication Technology, ICICT 2019
Country/Territory	United Kingdom
City	London
Period	27/02/19 → 28/02/19

Keywords

Deep learning
DNN
Intrusion detection system
Network security

Access to Document

10.1007/978-981-15-0637-6_40

Cite this

Liu, Z., Ghulam, M. U. D., Zhu, Y., Yan, X., Wang, L., Jiang, Z., & Luo, J. (2020). Deep learning approach for ids using dnn for network anomaly detection. In X.-S. Yang, S. Sherratt, N. Dey, & A. Joshi (Eds.), 4th International Congress on Information and Communication Technology, ICICT 2019, Volume 1 (pp. 471-479). (Advances in Intelligent Systems and Computing; Vol. 1041). Springer. https://doi.org/10.1007/978-981-15-0637-6_40

@inproceedings{c875d0e28f2b4c4b925cd01622d036dc,

title = "Deep learning approach for ids using dnn for network anomaly detection",

abstract = "With the astonishing development of the Internet and its applications in the last decade, cyberattacks are changing quickly, and the necessity of protection for communication network has improved tremendously. As the primary defense, the intrusion detection system plays a crucial role in making sure the network security. Key to intrusion detection system is actually to determine a variety of attacks effectively as well as to adjust to a constantly changing threat scenario. DNN or Deep Neural Network on NSL-KDD dataset for effective detection of an attack. Firstly, the dataset was preprocessed and normalized and then fed to the DNN algorithm to create a model. For testing purpose, entire dataset of NSL-KDD was used. Finally, to analyze the accuracy and precision of the DNN model, we use accuracy and precision matrices. The proposed DNN-based strategy enhances network anomaly detection and opens new analysis gateway for intrusion detection systems.",

keywords = "Deep learning, DNN, Intrusion detection system, Network security",

author = "Zhiqiang Liu and Ghulam, {Mohi Ud Din} and Ye Zhu and Xuanlin Yan and Lifang Wang and Zejun Jiang and Jianchao Luo",

note = "Publisher Copyright: {\textcopyright} Springer Nature Singapore Pte Ltd. 2020.; 4th International Congress on Information and Communication Technology, ICICT 2019 ; Conference date: 27-02-2019 Through 28-02-2019",

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isbn = "9789811506369",

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Liu, Z, Ghulam, MUD, Zhu, Y, Yan, X, Wang, L, Jiang, Z & Luo, J 2020, Deep learning approach for ids using dnn for network anomaly detection. in X-S Yang, S Sherratt, N Dey & A Joshi (eds), 4th International Congress on Information and Communication Technology, ICICT 2019, Volume 1. Advances in Intelligent Systems and Computing, vol. 1041, Springer, pp. 471-479, 4th International Congress on Information and Communication Technology, ICICT 2019, London, United Kingdom, 27/02/19. https://doi.org/10.1007/978-981-15-0637-6_40

Deep learning approach for ids using dnn for network anomaly detection. / Liu, Zhiqiang; Ghulam, Mohi Ud Din; Zhu, Ye et al.
4th International Congress on Information and Communication Technology, ICICT 2019, Volume 1. ed. / Xin-She Yang; Simon Sherratt; Nilanjan Dey; Amit Joshi. Springer, 2020. p. 471-479 (Advances in Intelligent Systems and Computing; Vol. 1041).

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

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AU - Liu, Zhiqiang

AU - Ghulam, Mohi Ud Din

AU - Zhu, Ye

AU - Yan, Xuanlin

AU - Wang, Lifang

AU - Jiang, Zejun

AU - Luo, Jianchao

N1 - Publisher Copyright: © Springer Nature Singapore Pte Ltd. 2020.

PY - 2020

Y1 - 2020

N2 - With the astonishing development of the Internet and its applications in the last decade, cyberattacks are changing quickly, and the necessity of protection for communication network has improved tremendously. As the primary defense, the intrusion detection system plays a crucial role in making sure the network security. Key to intrusion detection system is actually to determine a variety of attacks effectively as well as to adjust to a constantly changing threat scenario. DNN or Deep Neural Network on NSL-KDD dataset for effective detection of an attack. Firstly, the dataset was preprocessed and normalized and then fed to the DNN algorithm to create a model. For testing purpose, entire dataset of NSL-KDD was used. Finally, to analyze the accuracy and precision of the DNN model, we use accuracy and precision matrices. The proposed DNN-based strategy enhances network anomaly detection and opens new analysis gateway for intrusion detection systems.

AB - With the astonishing development of the Internet and its applications in the last decade, cyberattacks are changing quickly, and the necessity of protection for communication network has improved tremendously. As the primary defense, the intrusion detection system plays a crucial role in making sure the network security. Key to intrusion detection system is actually to determine a variety of attacks effectively as well as to adjust to a constantly changing threat scenario. DNN or Deep Neural Network on NSL-KDD dataset for effective detection of an attack. Firstly, the dataset was preprocessed and normalized and then fed to the DNN algorithm to create a model. For testing purpose, entire dataset of NSL-KDD was used. Finally, to analyze the accuracy and precision of the DNN model, we use accuracy and precision matrices. The proposed DNN-based strategy enhances network anomaly detection and opens new analysis gateway for intrusion detection systems.

KW - Deep learning

KW - DNN

KW - Intrusion detection system

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A2 - Dey, Nilanjan

A2 - Joshi, Amit

PB - Springer

T2 - 4th International Congress on Information and Communication Technology, ICICT 2019

Y2 - 27 February 2019 through 28 February 2019

ER -

Liu Z, Ghulam MUD, Zhu Y, Yan X, Wang L, Jiang Z et al. Deep learning approach for ids using dnn for network anomaly detection. In Yang XS, Sherratt S, Dey N, Joshi A, editors, 4th International Congress on Information and Communication Technology, ICICT 2019, Volume 1. Springer. 2020. p. 471-479. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-981-15-0637-6_40

Deep learning approach for ids using dnn for network anomaly detection

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Keywords

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