Artificial intelligence-enabled DDoS detection for blockchain-based smart transport systems.pdf (1.22 MB)
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Artificial intelligence-enabled DDoS detection for blockchain-based smart transport systems

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journal contribution
posted on 2023-01-24, 23:27 authored by Tong Liu, Fariza SabrinaFariza Sabrina, Julian Jang-Jaccard, Wen Xu, Yuanyuan Wei
A 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.

History

Volume

22

Issue

1

Start Page

1

End Page

22

Number of Pages

22

eISSN

1424-8239

ISSN

1424-8220

Publisher

MDPI AG

Publisher License

CC BY

Additional Rights

CC BY 4.0

Language

en

Peer Reviewed

Yes

Open Access

Yes

Acceptance Date

2021-12-18

Era Eligible

Yes

Medium

Electronic

Journal

Sensors

Article Number

32