A machine learning approach for intrusion detection in smart cities

Asmaa Elsaeidy; Kumudu S. Munasinghe; Dharmendra Sharma; Abbas Jamalipour

doi:10.1109/VTCFall.2019.8891281

A machine learning approach for intrusion detection in smart cities

Asmaa Elsaeidy
, Kumudu S. Munasinghe
, Dharmendra Sharma
, Abbas Jamalipour

Information Systems

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

15 Citations (Scopus)

Abstract

Over the recent years smart cities have been emerged as promising paradigm for a transition toward providing effective and real time smart services. Despite the great potential it brings to citizens' life, security and privacy issues still need to be addressed. Due to technology advances, large amount of data is produced, where machine learning methods are applied to learn meaningful patterns. In this paper a machine learning-based framework is proposed for detecting distributed Denial of Service (DDoS) attacks in smart cities. The proposed framework applies restricted Boltzmann machines to learn high-level features from raw data and on top of these learned features, a feed forward neural network model is trained for attack detection. The performance of the proposed framework is verified using a smart city dataset collected from a smart water plant. The results show the effectiveness of the proposed framework in detecting DDoS attacks.

Original language	English
Title of host publication	2019 IEEE 90th Vehicular Technology Conference, VTC 2019 Fall - Proceedings
Editors	Li-Chun Wang, Murat Uysal
Place of Publication	United States
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	1-5
Number of pages	5
ISBN (Electronic)	9781728112206
ISBN (Print)	9781728112213
DOIs	https://doi.org/10.1109/VTCFall.2019.8891281
Publication status	Published - 22 Sept 2019
Event	90th IEEE Vehicular Technology Conference - Honolulu, United States Duration: 22 Sept 2019 → 25 Sept 2019

Publication series

Name	IEEE Vehicular Technology Conference
Volume	2019-September
ISSN (Print)	1550-2252

Conference

Conference	90th IEEE Vehicular Technology Conference
Abbreviated title	VTC 2019
Country/Territory	United States
City	Honolulu
Period	22/09/19 → 25/09/19

UN SDGs

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

SDG 11 Sustainable Cities and Communities

Access to Document

10.1109/VTCFall.2019.8891281

Cite this

Elsaeidy, A., Munasinghe, K. S., Sharma, D., & Jamalipour, A. (2019). A machine learning approach for intrusion detection in smart cities. In L.-C. Wang, & M. Uysal (Eds.), 2019 IEEE 90th Vehicular Technology Conference, VTC 2019 Fall - Proceedings (pp. 1-5). Article 8891281 (IEEE Vehicular Technology Conference; Vol. 2019-September). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/VTCFall.2019.8891281

Elsaeidy, Asmaa ; Munasinghe, Kumudu S. ; Sharma, Dharmendra et al. / A machine learning approach for intrusion detection in smart cities. 2019 IEEE 90th Vehicular Technology Conference, VTC 2019 Fall - Proceedings. editor / Li-Chun Wang ; Murat Uysal. United States : IEEE, Institute of Electrical and Electronics Engineers, 2019. pp. 1-5 (IEEE Vehicular Technology Conference).

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title = "A machine learning approach for intrusion detection in smart cities",

abstract = "Over the recent years smart cities have been emerged as promising paradigm for a transition toward providing effective and real time smart services. Despite the great potential it brings to citizens' life, security and privacy issues still need to be addressed. Due to technology advances, large amount of data is produced, where machine learning methods are applied to learn meaningful patterns. In this paper a machine learning-based framework is proposed for detecting distributed Denial of Service (DDoS) attacks in smart cities. The proposed framework applies restricted Boltzmann machines to learn high-level features from raw data and on top of these learned features, a feed forward neural network model is trained for attack detection. The performance of the proposed framework is verified using a smart city dataset collected from a smart water plant. The results show the effectiveness of the proposed framework in detecting DDoS attacks.",

keywords = "Deep learning, Distributed Denial of Service, Intrusion detection, Smart city, Smart water plant",

author = "Asmaa Elsaeidy and Munasinghe, \{Kumudu S.\} and Dharmendra Sharma and Abbas Jamalipour",

year = "2019",

month = sep,

day = "22",

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editor = "Li-Chun Wang and Murat Uysal",

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address = "United States",

note = "90th IEEE Vehicular Technology Conference, VTC 2019 ; Conference date: 22-09-2019 Through 25-09-2019",

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Elsaeidy, A, Munasinghe, KS , Sharma, D & Jamalipour, A 2019, A machine learning approach for intrusion detection in smart cities. in L-C Wang & M Uysal (eds), 2019 IEEE 90th Vehicular Technology Conference, VTC 2019 Fall - Proceedings., 8891281, IEEE Vehicular Technology Conference, vol. 2019-September, IEEE, Institute of Electrical and Electronics Engineers, United States, pp. 1-5, 90th IEEE Vehicular Technology Conference, Honolulu, United States, 22/09/19. https://doi.org/10.1109/VTCFall.2019.8891281

A machine learning approach for intrusion detection in smart cities. / Elsaeidy, Asmaa; Munasinghe, Kumudu S.; Sharma, Dharmendra et al.
2019 IEEE 90th Vehicular Technology Conference, VTC 2019 Fall - Proceedings. ed. / Li-Chun Wang; Murat Uysal. United States: IEEE, Institute of Electrical and Electronics Engineers, 2019. p. 1-5 8891281 (IEEE Vehicular Technology Conference; Vol. 2019-September).

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

TY - GEN

T1 - A machine learning approach for intrusion detection in smart cities

AU - Elsaeidy, Asmaa

AU - Munasinghe, Kumudu S.

AU - Sharma, Dharmendra

AU - Jamalipour, Abbas

PY - 2019/9/22

Y1 - 2019/9/22

N2 - Over the recent years smart cities have been emerged as promising paradigm for a transition toward providing effective and real time smart services. Despite the great potential it brings to citizens' life, security and privacy issues still need to be addressed. Due to technology advances, large amount of data is produced, where machine learning methods are applied to learn meaningful patterns. In this paper a machine learning-based framework is proposed for detecting distributed Denial of Service (DDoS) attacks in smart cities. The proposed framework applies restricted Boltzmann machines to learn high-level features from raw data and on top of these learned features, a feed forward neural network model is trained for attack detection. The performance of the proposed framework is verified using a smart city dataset collected from a smart water plant. The results show the effectiveness of the proposed framework in detecting DDoS attacks.

AB - Over the recent years smart cities have been emerged as promising paradigm for a transition toward providing effective and real time smart services. Despite the great potential it brings to citizens' life, security and privacy issues still need to be addressed. Due to technology advances, large amount of data is produced, where machine learning methods are applied to learn meaningful patterns. In this paper a machine learning-based framework is proposed for detecting distributed Denial of Service (DDoS) attacks in smart cities. The proposed framework applies restricted Boltzmann machines to learn high-level features from raw data and on top of these learned features, a feed forward neural network model is trained for attack detection. The performance of the proposed framework is verified using a smart city dataset collected from a smart water plant. The results show the effectiveness of the proposed framework in detecting DDoS attacks.

KW - Deep learning

KW - Distributed Denial of Service

KW - Intrusion detection

KW - Smart city

KW - Smart water plant

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M3 - Conference contribution

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SN - 9781728112213

T3 - IEEE Vehicular Technology Conference

SP - 1

EP - 5

BT - 2019 IEEE 90th Vehicular Technology Conference, VTC 2019 Fall - Proceedings

A2 - Wang, Li-Chun

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PB - IEEE, Institute of Electrical and Electronics Engineers

CY - United States

T2 - 90th IEEE Vehicular Technology Conference

Y2 - 22 September 2019 through 25 September 2019

ER -

Elsaeidy A, Munasinghe KS , Sharma D, Jamalipour A. A machine learning approach for intrusion detection in smart cities. In Wang LC, Uysal M, editors, 2019 IEEE 90th Vehicular Technology Conference, VTC 2019 Fall - Proceedings. United States: IEEE, Institute of Electrical and Electronics Engineers. 2019. p. 1-5. 8891281. (IEEE Vehicular Technology Conference). doi: 10.1109/VTCFall.2019.8891281

A machine learning approach for intrusion detection in smart cities

Abstract

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UN SDGs

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