Abstract
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.
Highlights
Accepted: 18 December 2021The smart public transport system is an important part of the development of reliable smart city initiatives as it contributes to improving our mobility and significantly decreasing our carbon footprint
False Positive (FP) and False Negative (FN) slightly highlighted in the CICDDoS2019 and the BoT-IoT datasets, if we take into account the proportion of the FP + FN, they are almost negligent in the scale of a few thousand (e.g., Microsoft Structured Query Language (MSSQL)/Lightweight Directory Access Protocol (LDAP), MSSQL/UDP) or 10 thousand (e.g., DoS/distributed denial of service (DDoS), DDoS/DoS) among millions of samples tested, less than 1%
Our blockchain-based smart transport system uses smart contracts which are the digital contracts used among the participating entities
Summary
The smart public transport system (e.g., buses, taxis, subways, community scooters) is an important part of the development of reliable smart city initiatives as it contributes to improving our mobility and significantly decreasing our carbon footprint. To provide ongoing availability of critical infrastructure involved in the blockchain-enabled smart transport system, a more comprehensive approach to detect different categories of cyberattacks is required. We propose an AI-enabled DDoS Detection model for a blockchain-based public transport system that can withstand various cybersecurity attacks. The blockchain part of our proposed approach enables the protection of the smart transport system from any integrity attacks that attempt to modify sensitive transport maintenance data. The deep learning part of our proposed approach, which combines an autoencoder (AE) as a feature extractor and multi-layer perceptron (MLP) as a classifier, can detect and classify a wide range of DDoS attempts that can potentially block or halt the exchange of urgent and critical maintenance data across the stakeholders of the smart public transport system.
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