Intelligent Detection System Enabled Attack Probability Using Markov Chain in Aerial Networks

Khan, IU; Abdollahi, A; Alturki, R; Alshehri, MD; Ikram, MA; Alyamani, HJ; Khan, S

Intelligent Detection System Enabled Attack Probability Using Markov Chain in Aerial Networks

Khan, IU Abdollahi, A Alturki, R Alshehri, MD Ikram, MA Alyamani, HJ Khan, S

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Publisher:: Hindawi
Publication Type:: Journal Article
Citation:: Wireless Communications and Mobile Computing, 2021, 2021, pp. 1-9
Issue Date:: 2021-01-01

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Field	Value	Language
dc.contributor.author	Khan, IU
dc.contributor.author	Abdollahi, A
dc.contributor.author	Alturki, R
dc.contributor.author	Alshehri, MD
dc.contributor.author	Ikram, MA
dc.contributor.author	Alyamani, HJ
dc.contributor.author	Khan, S
dc.date.accessioned	2022-05-05T20:32:52Z
dc.date.available	2022-05-05T20:32:52Z
dc.date.issued	2021-01-01
dc.identifier.citation	Wireless Communications and Mobile Computing, 2021, 2021, pp. 1-9
dc.identifier.issn	1530-8669
dc.identifier.issn	1530-8677
dc.identifier.uri	http://hdl.handle.net/10453/157067
dc.description.abstract	The Internet of Things (IoT) plays an important role to connect people, data, processes, and things. From linked supply chains to big data produced by a large number of IoT devices to industrial control systems where cybersecurity has become a critical problem in IoT-powered systems. Denial of Service (DoS), distributed denial of service (DDoS), and ping of death attacks are significant threats to flying networks. This paper presents an intrusion detection system (IDS) based on attack probability using the Markov chain to detect flooding attacks. While the paper includes buffer queue length by using queuing theory concept to evaluate the network safety. Also, the network scenario will change due to the dynamic nature of flying vehicles. Simulation describes the queue length when the ground station is under attack. The proposed IDS utilizes the optimal threshold to make a tradeoff between false positive and false negative states with Markov binomial and Markov chain distribution stochastic models. However, at each time slot, the results demonstrate maintaining queue length in normal mode with less packet loss and high attack detection.
dc.language	English
dc.publisher	Hindawi
dc.relation.ispartof	Wireless Communications and Mobile Computing
dc.relation.isbasedon	10.1155/2021/1542657
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0805 Distributed Computing, 0906 Electrical and Electronic Engineering, 1005 Communications Technologies
dc.subject.classification	Networking & Telecommunications
dc.title	Intelligent Detection System Enabled Attack Probability Using Markov Chain in Aerial Networks
dc.type	Journal Article
utslib.citation.volume	2021
utslib.for	0805 Distributed Computing
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	1005 Communications Technologies
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
dc.date.updated	2022-05-05T20:32:50Z
pubs.publication-status	Published
pubs.volume	2021

Abstract:

The Internet of Things (IoT) plays an important role to connect people, data, processes, and things. From linked supply chains to big data produced by a large number of IoT devices to industrial control systems where cybersecurity has become a critical problem in IoT-powered systems. Denial of Service (DoS), distributed denial of service (DDoS), and ping of death attacks are significant threats to flying networks. This paper presents an intrusion detection system (IDS) based on attack probability using the Markov chain to detect flooding attacks. While the paper includes buffer queue length by using queuing theory concept to evaluate the network safety. Also, the network scenario will change due to the dynamic nature of flying vehicles. Simulation describes the queue length when the ground station is under attack. The proposed IDS utilizes the optimal threshold to make a tradeoff between false positive and false negative states with Markov binomial and Markov chain distribution stochastic models. However, at each time slot, the results demonstrate maintaining queue length in normal mode with less packet loss and high attack detection.

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http://hdl.handle.net/10453/157067