Citation: Algarni, A.; Thayananthan, V.Autonomous Vehicles: The Cybersecurity Vulnerabilities and Countermeasures for Big Data Communication. Symmetry 2022, 14, 2494. https://doi.org/10.3390/ sym14122494 Academic Editors: Sergei D. Odintsov, Alexander Zaslavski and Adam Glowacz Received: 19 September 2022 Accepted: 20 November 2022 Published: 24 November 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. 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/). symmetry S S Article Autonomous Vehicles: The Cybersecurity Vulnerabilities and Countermeasures for Big Data Communication Abdullah Algarni * and Vijey Thayananthan Computer Science Department, King Abdulaziz University, Jeddah 21589, Saudi Arabia * Correspondence: amsalgarni@kau.edu.sa; Tel.: +966-12-6952000 Abstract: The possible applications of communication based on big data have steadily increased in several industries, such as the autonomous vehicle industry, with a corresponding increase in security challenges, including cybersecurity vulnerabilities (CVs). The cybersecurity-related symmetry of big data communication systems used in autonomous vehicles may raise more vulnerabilities in the data communication process between these vehicles and IoT devices. The data involved in the CVs may be encrypted using an asymmetric and symmetric algorithm. Autonomous vehicles with proactive cybersecurity solutions, power-based cyberattacks, and dynamic countermeasures are the modern issues/developments with emerging technology and evolving attacks. Research on big data has been primarily focused on mitigating CVs and minimizing big data breaches using appropriate countermeasures known as security solutions. In the future, CVs in data communication between autonomous vehicles (DCAV), the weaknesses of autonomous vehicular networks (AVN), and cyber threats to network functions form the primary security issues in big data communication, AVN, and DCAV. Therefore, efficient countermeasure models and security algorithms are required to minimize CVs and data breaches. As a technique, policies and rules of CVs with proxy and demilitarized zone (DMZ) servers were combined to enhance the efficiency of the countermeasure. In this study, we propose an information security approach that depends on the increasing energy levels of attacks and CVs by identifying the energy levels of each attack. To show the results of the performance of our proposed countermeasure, CV and energy consumption are compared with different attacks. Thus, the countermeasures can secure big data communication and DCAV using security algorithms related to cybersecurity and effectively prevent CVs and big data breaches during data communication. Keywords: cybersecurity vulnerabilities; autonomous vehicles; vehicular communications; security solutions 1. Introduction All future systems will have automated features that allow users, including researchers, to study several automation functions before they operate autonomous systems, such as autonomous vehicles (AVs). All features of AVs, such as functions and connections, safety warnings, and privacy issues, are expected to be secured prior to operation. However, cybersecurity vulnerabilities (CVs) affect these features leading to improper functioning or failed connections. Cyberattacks; threats; and physical failures of unreliable compo- nents, systems, and communication services that depend on energy efficiency (EE) further compromise the integrity of AVs. Due to the symmetry between IoT device vulnerabilities and data communication, cybersecurity is becoming an increasingly significant factor in autonomous vehicles and related data communication systems. Researchers have attempted to design countermeasures for malware infections and cy- berattacks, that exploit CVs in data communication between autonomous vehicles (DCAV) and autonomous vehicular networks (AVN) by determining the origin of attacks and an- alyzing CVs. To detect infections and cyberattacks, the possible threats to the AVN and DCAV need to be predicted. In addition, the energy variations of the predicted attacks must Symmetry 2022, 14, 2494. https://doi.org/10.3390/sym14122494 https://www.mdpi.com/journal/symmetry