Artificial intelligence-enabled DDoS detection for blockchain-based smart transport systems.pdf (1.22 MB)
Artificial intelligence-enabled DDoS detection for blockchain-based smart transport systems
journal contribution
posted on 2023-01-24, 23:27 authored by Tong Liu, Fariza SabrinaFariza Sabrina, Julian Jang-Jaccard, Wen Xu, Yuanyuan WeiA 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
22Issue
1Start Page
1End Page
22Number of Pages
22eISSN
1424-8239ISSN
1424-8220Publisher
MDPI AGPublisher License
CC BYPublisher DOI
Full Text URL
Additional Rights
CC BY 4.0Language
enPeer Reviewed
- Yes
Open Access
- Yes
Acceptance Date
2021-12-18Era Eligible
- Yes