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

Tong Liu¹, Fariza Sabrina², Julian Jang-Jaccard³

¹College of Sciences, Massey University, Auckland 0632, New Zealand.

Sensors (Basel, Switzerland)

|January 11, 2022

Summary

This summary is machine-generated.

This study introduces a novel cybersecurity approach for smart public transport using blockchain and deep learning (DL). The combined method enhances data integrity and detects denial-of-service attacks, improving system security.

Keywords:

DDoS artificial intelligence autoencoder blockchain deep learning multi-layer perceptron smart contract smart transport system

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Area of Science:

Cybersecurity
Artificial Intelligence
Transportation Systems

Background:

Smart public transport systems are crucial for mobility and reducing carbon footprints.
Ensuring the safety and maintenance of these systems against cyberattacks is paramount.
Existing security measures may not fully address the complex threats faced by integrated transport networks.

Purpose of the Study:

To propose a novel, integrated approach for enhancing the cybersecurity of smart public transport systems.
To leverage blockchain technology for data integrity and deep learning for threat detection.
To provide a comprehensive security solution against data forgery and denial-of-service attacks.

Main Methods:

Implementation of a blockchain framework for secure data transactions and integrity.
Development of a hybrid deep learning model combining Autoencoder (AE) and Multi-layer Perceptron (MLP).
Evaluation of the deep learning model on diverse datasets (CICDDoS2019, CIC-IDS2017, BoT-IoT) for Distributed Denial of Service (DDoS) detection.

Main Results:

The proposed blockchain ensures the integrity of transport maintenance data against unauthorized modifications.
The hybrid deep learning model effectively detects a wide range of DDoS attacks.
The model achieved an average F1-score exceeding 95% across three benchmark datasets.
Experimental results demonstrate superior performance compared to existing security methods.

Conclusions:

The integrated blockchain and deep learning approach offers robust protection for smart public transport systems.
This novel method significantly enhances security against data integrity attacks and DDoS threats.
The findings support the adoption of advanced cybersecurity measures for critical infrastructure like smart transportation.