A Hybrid Deep Learning Approach for Replay and DDoS Attack Detection in a Smart City

Today’s smart city infrastructure is predominantly dependant on Internet of Things (IoT) technologies. IoT technology essentially facilitates a platform for service automation through connections of heterogeneous objects via the Internet backbone. However, the security issues associated w...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Asmaa A. Elsaeidy, Abbas Jamalipour, Kumudu S. Munasinghe
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Intrusion detection distributed denial of service (DDoS) attacks replay attack smart city deep learning Internet of Things (IoT) Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/2df9c71157ef415e86e71285e439f94e
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:2df9c71157ef415e86e71285e439f94e
record_format	dspace
spelling	oai:doaj.org-article:2df9c71157ef415e86e71285e439f94e2021-11-25T00:00:40ZA Hybrid Deep Learning Approach for Replay and DDoS Attack Detection in a Smart City2169-353610.1109/ACCESS.2021.3128701https://doaj.org/article/2df9c71157ef415e86e71285e439f94e2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9617591/https://doaj.org/toc/2169-3536Today’s smart city infrastructure is predominantly dependant on Internet of Things (IoT) technologies. IoT technology essentially facilitates a platform for service automation through connections of heterogeneous objects via the Internet backbone. However, the security issues associated with IoT networks make smart city infrastructure vulnerable to cyber-attacks. For example, Distributed Denial of Service (DDoS) attack violates the authorization conditions in smart city infrastructure; whereas replay attack violates the authentication conditions in smart city infrastructure. Both attacks lead to physical disruption to smart city infrastructure, which may even lead to financial loss and/or loss of human lives. In this paper, a hybrid deep learning model is developed for detecting replay and DDoS attacks in a real life smart city platform. The performance of the proposed hybrid model is evaluated using real life smart city datasets (environmental, smart river and smart soil), where DDoS and replay attacks were simulated. The proposed model reported high accuracy rates: 98.37% for the environmental dataset, 98.13% for the smart river dataset, and 99.51% for the smart soil dataset. The results demonstrated an improved performance of the proposed model over other machine learning and deep learning models from the literature.Asmaa A. ElsaeidyAbbas JamalipourKumudu S. MunasingheIEEEarticleIntrusion detectiondistributed denial of service (DDoS) attacksreplay attacksmart citydeep learningInternet of Things (IoT)Electrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 154864-154875 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Intrusion detection distributed denial of service (DDoS) attacks replay attack smart city deep learning Internet of Things (IoT) Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Intrusion detection distributed denial of service (DDoS) attacks replay attack smart city deep learning Internet of Things (IoT) Electrical engineering. Electronics. Nuclear engineering TK1-9971 Asmaa A. Elsaeidy Abbas Jamalipour Kumudu S. Munasinghe A Hybrid Deep Learning Approach for Replay and DDoS Attack Detection in a Smart City
description	Today’s smart city infrastructure is predominantly dependant on Internet of Things (IoT) technologies. IoT technology essentially facilitates a platform for service automation through connections of heterogeneous objects via the Internet backbone. However, the security issues associated with IoT networks make smart city infrastructure vulnerable to cyber-attacks. For example, Distributed Denial of Service (DDoS) attack violates the authorization conditions in smart city infrastructure; whereas replay attack violates the authentication conditions in smart city infrastructure. Both attacks lead to physical disruption to smart city infrastructure, which may even lead to financial loss and/or loss of human lives. In this paper, a hybrid deep learning model is developed for detecting replay and DDoS attacks in a real life smart city platform. The performance of the proposed hybrid model is evaluated using real life smart city datasets (environmental, smart river and smart soil), where DDoS and replay attacks were simulated. The proposed model reported high accuracy rates: 98.37% for the environmental dataset, 98.13% for the smart river dataset, and 99.51% for the smart soil dataset. The results demonstrated an improved performance of the proposed model over other machine learning and deep learning models from the literature.
format	article
author	Asmaa A. Elsaeidy Abbas Jamalipour Kumudu S. Munasinghe
author_facet	Asmaa A. Elsaeidy Abbas Jamalipour Kumudu S. Munasinghe
author_sort	Asmaa A. Elsaeidy
title	A Hybrid Deep Learning Approach for Replay and DDoS Attack Detection in a Smart City
title_short	A Hybrid Deep Learning Approach for Replay and DDoS Attack Detection in a Smart City
title_full	A Hybrid Deep Learning Approach for Replay and DDoS Attack Detection in a Smart City
title_fullStr	A Hybrid Deep Learning Approach for Replay and DDoS Attack Detection in a Smart City
title_full_unstemmed	A Hybrid Deep Learning Approach for Replay and DDoS Attack Detection in a Smart City
title_sort	hybrid deep learning approach for replay and ddos attack detection in a smart city
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/2df9c71157ef415e86e71285e439f94e
work_keys_str_mv	AT asmaaaelsaeidy ahybriddeeplearningapproachforreplayandddosattackdetectioninasmartcity AT abbasjamalipour ahybriddeeplearningapproachforreplayandddosattackdetectioninasmartcity AT kumudusmunasinghe ahybriddeeplearningapproachforreplayandddosattackdetectioninasmartcity AT asmaaaelsaeidy hybriddeeplearningapproachforreplayandddosattackdetectioninasmartcity AT abbasjamalipour hybriddeeplearningapproachforreplayandddosattackdetectioninasmartcity AT kumudusmunasinghe hybriddeeplearningapproachforreplayandddosattackdetectioninasmartcity
_version_	1718414686617075712

A Hybrid Deep Learning Approach for Replay and DDoS Attack Detection in a Smart City

Ejemplares similares