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Cyber Attack Detection for Self-Driving Vehicle Networks Using Deep Autoencoder Algorithms

Connected and autonomous vehicles (CAVs) present exciting opportunities for the improvement of both the mobility of people and the efficiency of transportation systems. The small computers in autonomous vehicles (CAVs) are referred to as electronic control units (ECUs) and are often perceived as bei...

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Autores principales: Alsaade, Fawaz Waselallah, Al-Adhaileh, Mosleh Hmoud
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10142967/
https://www.ncbi.nlm.nih.gov/pubmed/37112429
http://dx.doi.org/10.3390/s23084086
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author Alsaade, Fawaz Waselallah
Al-Adhaileh, Mosleh Hmoud
author_facet Alsaade, Fawaz Waselallah
Al-Adhaileh, Mosleh Hmoud
author_sort Alsaade, Fawaz Waselallah
collection PubMed
description Connected and autonomous vehicles (CAVs) present exciting opportunities for the improvement of both the mobility of people and the efficiency of transportation systems. The small computers in autonomous vehicles (CAVs) are referred to as electronic control units (ECUs) and are often perceived as being a component of a broader cyber–physical system. Subsystems of ECUs are often networked together via a variety of in-vehicle networks (IVNs) so that data may be exchanged, and the vehicle can operate more efficiently. The purpose of this work is to explore the use of machine learning and deep learning methods in defence against cyber threats to autonomous cars. Our primary emphasis is on identifying erroneous information implanted in the data buses of various automobiles. In order to categorise this type of erroneous data, the gradient boosting method is used, providing a productive illustration of machine learning. To examine the performance of the proposed model, two real datasets, namely the Car-Hacking and UNSE-NB15 datasets, were used. Real automated vehicle network datasets were used in the verification process of the proposed security solution. These datasets included spoofing, flooding and replay attacks, as well as benign packets. The categorical data were transformed into numerical form via pre-processing. Machine learning and deep learning algorithms, namely k-nearest neighbour (KNN) and decision trees, long short-term memory (LSTM), and deep autoencoders, were employed to detect CAN attacks. According to the findings of the experiments, using the decision tree and KNN algorithms as machine learning approaches resulted in accuracy levels of 98.80% and 99%, respectively. On the other hand, the use of LSTM and deep autoencoder algorithms as deep learning approaches resulted in accuracy levels of 96% and 99.98%, respectively. The maximum accuracy was achieved when using the decision tree and deep autoencoder algorithms. Statistical analysis methods were used to analyse the results of the classification algorithms, and the determination coefficient measurement for the deep autoencoder was found to reach a value of R(2) = 95%. The performance of all of the models that were built in this way surpassed that of those already in use, with almost perfect levels of accuracy being achieved. The system developed is able to overcome security issues in IVNs.
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spelling pubmed-101429672023-04-29 Cyber Attack Detection for Self-Driving Vehicle Networks Using Deep Autoencoder Algorithms Alsaade, Fawaz Waselallah Al-Adhaileh, Mosleh Hmoud Sensors (Basel) Article Connected and autonomous vehicles (CAVs) present exciting opportunities for the improvement of both the mobility of people and the efficiency of transportation systems. The small computers in autonomous vehicles (CAVs) are referred to as electronic control units (ECUs) and are often perceived as being a component of a broader cyber–physical system. Subsystems of ECUs are often networked together via a variety of in-vehicle networks (IVNs) so that data may be exchanged, and the vehicle can operate more efficiently. The purpose of this work is to explore the use of machine learning and deep learning methods in defence against cyber threats to autonomous cars. Our primary emphasis is on identifying erroneous information implanted in the data buses of various automobiles. In order to categorise this type of erroneous data, the gradient boosting method is used, providing a productive illustration of machine learning. To examine the performance of the proposed model, two real datasets, namely the Car-Hacking and UNSE-NB15 datasets, were used. Real automated vehicle network datasets were used in the verification process of the proposed security solution. These datasets included spoofing, flooding and replay attacks, as well as benign packets. The categorical data were transformed into numerical form via pre-processing. Machine learning and deep learning algorithms, namely k-nearest neighbour (KNN) and decision trees, long short-term memory (LSTM), and deep autoencoders, were employed to detect CAN attacks. According to the findings of the experiments, using the decision tree and KNN algorithms as machine learning approaches resulted in accuracy levels of 98.80% and 99%, respectively. On the other hand, the use of LSTM and deep autoencoder algorithms as deep learning approaches resulted in accuracy levels of 96% and 99.98%, respectively. The maximum accuracy was achieved when using the decision tree and deep autoencoder algorithms. Statistical analysis methods were used to analyse the results of the classification algorithms, and the determination coefficient measurement for the deep autoencoder was found to reach a value of R(2) = 95%. The performance of all of the models that were built in this way surpassed that of those already in use, with almost perfect levels of accuracy being achieved. The system developed is able to overcome security issues in IVNs. MDPI 2023-04-18 /pmc/articles/PMC10142967/ /pubmed/37112429 http://dx.doi.org/10.3390/s23084086 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Alsaade, Fawaz Waselallah
Al-Adhaileh, Mosleh Hmoud
Cyber Attack Detection for Self-Driving Vehicle Networks Using Deep Autoencoder Algorithms
title Cyber Attack Detection for Self-Driving Vehicle Networks Using Deep Autoencoder Algorithms
title_full Cyber Attack Detection for Self-Driving Vehicle Networks Using Deep Autoencoder Algorithms
title_fullStr Cyber Attack Detection for Self-Driving Vehicle Networks Using Deep Autoencoder Algorithms
title_full_unstemmed Cyber Attack Detection for Self-Driving Vehicle Networks Using Deep Autoencoder Algorithms
title_short Cyber Attack Detection for Self-Driving Vehicle Networks Using Deep Autoencoder Algorithms
title_sort cyber attack detection for self-driving vehicle networks using deep autoencoder algorithms
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10142967/
https://www.ncbi.nlm.nih.gov/pubmed/37112429
http://dx.doi.org/10.3390/s23084086
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