Next Article in Journal
Entrainment of Weakly Coupled Canonical Oscillators with Applications in Gradient Frequency Neural Networks Using Approximating Analytical Methods
Next Article in Special Issue
Fast COVID-19 and Pneumonia Classification Using Chest X-ray Images
Previous Article in Journal
The Harmonic Mapping Whose Hopf Differential Is a Constant
Previous Article in Special Issue
Entropy-Randomized Forecasting of Stochastic Dynamic Regression Models
Open AccessArticle

Machine Learning-Based Detection for Cyber Security Attacks on Connected and Autonomous Vehicles

by Qiyi He 1,*, Xiaolin Meng 1, Rong Qu 2 and Ruijie Xi 1,3
Nottingham Geospatial Institute, University of Nottingham, Nottingham NG7 2TU, UK
School of Computer Science, University of Nottingham, Nottingham NG8 1BB, UK
GNSS Research Center, Wuhan University, Wuhan 430079, China
Author to whom correspondence should be addressed.
Mathematics 2020, 8(8), 1311;
Received: 2 June 2020 / Revised: 23 July 2020 / Accepted: 5 August 2020 / Published: 7 August 2020
(This article belongs to the Special Issue Machine Learning and Data Mining in Pattern Recognition)
Connected and Autonomous Vehicle (CAV)-related initiatives have become some of the fastest expanding in recent years, and have started to affect the daily lives of people. More and more companies and research organizations have announced their initiatives, and some have started CAV road trials. Governments around the world have also introduced policies to support and accelerate the deployments of CAVs. Along these, issues such as CAV cyber security have become predominant, forming an essential part of the complications of CAV deployment. There is, however, no universally agreed upon or recognized framework for CAV cyber security. In this paper, following the UK CAV cyber security principles, we propose a UML (Unified Modeling Language)-based CAV cyber security framework, and based on which we classify the potential vulnerabilities of CAV systems. With this framework, a new CAV communication cyber-attack data set (named CAV-KDD) is generated based on the widely tested benchmark data set KDD99. This data set focuses on the communication-based CAV cyber-attacks. Two classification models are developed, using two machine learning algorithms, namely Decision Tree and Naive Bayes, based on the CAV-KDD training data set. The accuracy, precision and runtime of these two models when identifying each type of communication-based attacks are compared and analysed. It is found that the Decision Tree model requires a shorter runtime, and is more appropriate for CAV communication attack detection. View Full-Text
Keywords: Connected and Autonomous Vehicle; cyber security; machine learning Connected and Autonomous Vehicle; cyber security; machine learning
Show Figures

Figure 1

MDPI and ACS Style

He, Q.; Meng, X.; Qu, R.; Xi, R. Machine Learning-Based Detection for Cyber Security Attacks on Connected and Autonomous Vehicles. Mathematics 2020, 8, 1311.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Search more from Scilit
Back to TopTop