Artificial Intelligence-Enabled DDoS Detection for Blockchain-Based Smart Transport Systems.

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dc.citation.issue1
dc.citation.volume22
dc.contributor.authorLiu T
dc.contributor.authorSabrina F
dc.contributor.authorJang-Jaccard J
dc.contributor.authorXu W
dc.contributor.authorWei Y
dc.date.accessioned2023-11-20T01:38:26Z
dc.date.available2022-01
dc.date.available2021-12-18
dc.date.available2023-11-20T01:38:26Z
dc.date.issued2021-12-22
dc.description.abstractA smart public transport system is expected to be an integral part of our human lives to improve our mobility and reduce the effect of our carbon footprint. The safety and ongoing maintenance of the smart public transport system from cyberattacks are vitally important. To provide more comprehensive protection against potential cyberattacks, we propose a novel approach that combines blockchain technology and a deep learning method that can better protect the smart public transport system. By the creation of signed and verified blockchain blocks and chaining of hashed blocks, the blockchain in our proposal can withstand unauthorized integrity attack that tries to forge sensitive transport maintenance data and transactions associated with it. A hybrid deep learning-based method, which combines autoencoder (AE) and multi-layer perceptron (MLP), in our proposal can effectively detect distributed denial of service (DDoS) attempts that can halt or block the urgent and critical exchange of transport maintenance data across the stakeholders. The experimental results of the hybrid deep learning evaluated on three different datasets (i.e., CICDDoS2019, CIC-IDS2017, and BoT-IoT) show that our deep learning model is effective to detect a wide range of DDoS attacks achieving more than 95% F1-score across all three datasets in average. The comparison of our approach with other similar methods confirms that our approach covers a more comprehensive range of security properties for the smart public transport system.
dc.description.publication-statusPublished
dc.identifierhttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000742029000001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=c5bb3b2499afac691c2e3c1a83ef6fef
dc.identifierARTN 32
dc.identifier.citationSENSORS, 2022, 22 (1)
dc.identifier.doi10.3390/s22010032
dc.identifier.eissn1424-8220
dc.identifier.elements-id450179
dc.identifier.harvestedMassey_Dark
dc.identifier.urihttps://hdl.handle.net/10179/17425
dc.publisherMDPI (Basel, Switzerland)
dc.relation.isPartOfSENSORS
dc.rightsCC BY 4.0
dc.subjectsmart transport system
dc.subjectblockchain
dc.subjectsmart contract
dc.subjectartificial intelligence
dc.subjectdeep learning
dc.subjectautoencoder
dc.subjectmulti-layer perceptron
dc.subjectDDoS
dc.subject.anzsrc0502 Environmental Science and Management
dc.subject.anzsrc0602 Ecology
dc.subject.anzsrc0301 Analytical Chemistry
dc.subject.anzsrc0805 Distributed Computing
dc.subject.anzsrc0906 Electrical and Electronic Engineering
dc.titleArtificial Intelligence-Enabled DDoS Detection for Blockchain-Based Smart Transport Systems.
dc.typeJournal article
pubs.notesNot known
pubs.organisational-group/Massey University
pubs.organisational-group/Massey University/College of Sciences
pubs.organisational-group/Massey University/College of Sciences/School of Mathematical and Computational Sciences
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