A Review: Cybersecurity Challenges and their Solutions in Connected and Autonomous Vehicles (CAVs)

Author:

Saeed Zubair,Masood Mubashir,Khan Misha Urooj

Abstract

Connected and Autonomous Vehicles (CAVs) are a crucial breakthrough in the automotive industry and a magnificent step toward a safe, secure, and intelligent transportation system (ITS). CAVs offer tremendous benefits to our society and environment, such as mitigation of traffic accidents, reduction in traffic congestion, fewer emissions of harmful gases, etc. However, emerging automotive technology also has some serious safety concerns. One of them is cyber security. Conventional vehicles are less prone to cyber-attacks, but CAVs are more susceptible to such events as they communicate with the surrounding infrastructure and other vehicles. To gather data for a better perception of their surroundings, CAVs are outfitted with state-of-the-art sensors and modules like LiDAR, GPS, RADAR, onboard computers, cameras, etc. Hackers, terrorist organizations, and vandals can manipulate this sensor data or may access the primary control by cyber-attack, which may result in enormous fatalities. The automotive industry must put up a rigid framework against cyber invasions to make CAVs a more reliable and secure means of transportation. This paper provides an overview of cybersecurity challenges in CAVs at the module and software levels. The sources of active and passive threats are analyzed. Finally, a feasible solution is recommended to cope with such threats

Publisher

Lembaga Penelitian dan Pengabdian kepada Masyarakat ITS

Subject

General Earth and Planetary Sciences,General Environmental Science

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Automated Vehicles and Infrastructure Enablers: Cybersecurity;2024-08-26

2. Enchanced CAV Security Using Machine Learning;2024 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD);2024-08-01

3. Cancerous and Non-Cancerous MRI Classification Using Dual DCNN Approach;Bioengineering;2024-04-23

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