Deep Learning Models for Cyber Security in IoT Networks

Monika Roopak; Gui Yun Tian; Jonathon Chambers

doi:10.1109/CCWC.2019.8666588

Deep Learning Models for Cyber Security in IoT Networks

Monika Roopak, Gui Yun Tian, Jonathon Chambers

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

299 Citations (Scopus)

Abstract

In this paper we propose deep learning models for the cyber security in IoT (Internet of Things) networks. IoT network is as a promising technology which connects the living and non-living things around the world. The implementation of IoT is growing fast but the cyber security is still a loophole, so it is susceptible to many cyber-attack and for the success of any network it most important that the network is completely secure, otherwise people could be reluctant to use this technology. DDoS (Distributed Denial of Service) attack has affected many IoT networks in recent past that has resulted in huge losses. We have proposed deep learning models and evaluated those using latest CICIDS2017 datasets for DDoS attack detection which has provided highest accuracy as 97.16% also proposed models are compared with machine learning algorithms. This paper also identifies open research challenges for usage of deep learning algorithm for IoT cyber security.

Original language	English
Title of host publication	2019 IEEE 9th Annual Computing and Communication Workshop and Conference, CCWC 2019
Editors	Satyajit Chakrabarti, Himadri Nath Saha
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	452-457
Number of pages	6
ISBN (Electronic)	9781728105543, 9781728105536
ISBN (Print)	9781728105550
DOIs	https://doi.org/10.1109/CCWC.2019.8666588
Publication status	Published - 14 Mar 2019
Externally published	Yes
Event	9th IEEE Annual Computing and Communication Workshop and Conference - Las Vegas, United States Duration: 7 Jan 2019 → 9 Jan 2019 Conference number: 9

Conference

Conference	9th IEEE Annual Computing and Communication Workshop and Conference
Abbreviated title	CCWC 2019
Country/Territory	United States
City	Las Vegas
Period	7/01/19 → 9/01/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/CCWC.2019.8666588Licence: Unspecified

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title = "Deep Learning Models for Cyber Security in IoT Networks",

abstract = "In this paper we propose deep learning models for the cyber security in IoT (Internet of Things) networks. IoT network is as a promising technology which connects the living and non-living things around the world. The implementation of IoT is growing fast but the cyber security is still a loophole, so it is susceptible to many cyber-attack and for the success of any network it most important that the network is completely secure, otherwise people could be reluctant to use this technology. DDoS (Distributed Denial of Service) attack has affected many IoT networks in recent past that has resulted in huge losses. We have proposed deep learning models and evaluated those using latest CICIDS2017 datasets for DDoS attack detection which has provided highest accuracy as 97.16% also proposed models are compared with machine learning algorithms. This paper also identifies open research challenges for usage of deep learning algorithm for IoT cyber security.",

keywords = "CNN, DDoS, Deep Learning, IoT, LSTM, RNN CICIDS2017",

author = "Monika Roopak and {Yun Tian}, Gui and Jonathon Chambers",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 9th IEEE Annual Computing and Communication Workshop and Conference, CCWC 2019 ; Conference date: 07-01-2019 Through 09-01-2019",

year = "2019",

month = mar,

day = "14",

doi = "10.1109/CCWC.2019.8666588",

language = "English",

isbn = "9781728105550",

pages = "452--457",

editor = "Satyajit Chakrabarti and Saha, {Himadri Nath}",

booktitle = "2019 IEEE 9th Annual Computing and Communication Workshop and Conference, CCWC 2019",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

}

Roopak, M, Yun Tian, G & Chambers, J 2019, Deep Learning Models for Cyber Security in IoT Networks. in S Chakrabarti & HN Saha (eds), 2019 IEEE 9th Annual Computing and Communication Workshop and Conference, CCWC 2019., 8666588, Institute of Electrical and Electronics Engineers Inc., pp. 452-457, 9th IEEE Annual Computing and Communication Workshop and Conference, Las Vegas, Nevada, United States, 7/01/19. https://doi.org/10.1109/CCWC.2019.8666588

Deep Learning Models for Cyber Security in IoT Networks. / Roopak, Monika; Yun Tian, Gui; Chambers, Jonathon.

2019 IEEE 9th Annual Computing and Communication Workshop and Conference, CCWC 2019. ed. / Satyajit Chakrabarti; Himadri Nath Saha. Institute of Electrical and Electronics Engineers Inc., 2019. p. 452-457 8666588.

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

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T1 - Deep Learning Models for Cyber Security in IoT Networks

AU - Roopak, Monika

AU - Yun Tian, Gui

AU - Chambers, Jonathon

N1 - Conference code: 9

PY - 2019/3/14

Y1 - 2019/3/14

N2 - In this paper we propose deep learning models for the cyber security in IoT (Internet of Things) networks. IoT network is as a promising technology which connects the living and non-living things around the world. The implementation of IoT is growing fast but the cyber security is still a loophole, so it is susceptible to many cyber-attack and for the success of any network it most important that the network is completely secure, otherwise people could be reluctant to use this technology. DDoS (Distributed Denial of Service) attack has affected many IoT networks in recent past that has resulted in huge losses. We have proposed deep learning models and evaluated those using latest CICIDS2017 datasets for DDoS attack detection which has provided highest accuracy as 97.16% also proposed models are compared with machine learning algorithms. This paper also identifies open research challenges for usage of deep learning algorithm for IoT cyber security.

AB - In this paper we propose deep learning models for the cyber security in IoT (Internet of Things) networks. IoT network is as a promising technology which connects the living and non-living things around the world. The implementation of IoT is growing fast but the cyber security is still a loophole, so it is susceptible to many cyber-attack and for the success of any network it most important that the network is completely secure, otherwise people could be reluctant to use this technology. DDoS (Distributed Denial of Service) attack has affected many IoT networks in recent past that has resulted in huge losses. We have proposed deep learning models and evaluated those using latest CICIDS2017 datasets for DDoS attack detection which has provided highest accuracy as 97.16% also proposed models are compared with machine learning algorithms. This paper also identifies open research challenges for usage of deep learning algorithm for IoT cyber security.

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KW - DDoS

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KW - LSTM

KW - RNN CICIDS2017

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M3 - Conference contribution

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ER -

Deep Learning Models for Cyber Security in IoT Networks

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