Securing the Future: A Comprehensive Review of Security Challenges and Solutions in Advanced Driver Assistance Systems

Advanced Driver Assistance Systems (ADAS) are advanced technologies that assist drivers with vehicle operation and navigation. Recent improvements and brisk expansion in the ADAS market, as well as an increase in the frequency of incidents such as sensor spoofing, communication interruption etc., in...

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Main Authors: Aryan Alpesh Mehta, Ali Asgar Padaria, Dwij Jayesh Bavisi, Vijay Ukani, Priyank Thakkar, Rebekah Geddam, Ketan Kotecha, Ajith Abraham
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/10373843/
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author Aryan Alpesh Mehta
Ali Asgar Padaria
Dwij Jayesh Bavisi
Vijay Ukani
Priyank Thakkar
Rebekah Geddam
Ketan Kotecha
Ajith Abraham
author_facet Aryan Alpesh Mehta
Ali Asgar Padaria
Dwij Jayesh Bavisi
Vijay Ukani
Priyank Thakkar
Rebekah Geddam
Ketan Kotecha
Ajith Abraham
author_sort Aryan Alpesh Mehta
collection DOAJ
description Advanced Driver Assistance Systems (ADAS) are advanced technologies that assist drivers with vehicle operation and navigation. Recent improvements and brisk expansion in the ADAS market, as well as an increase in the frequency of incidents such as sensor spoofing, communication interruption etc., in autonomous vehicles (AVs), have raised the need to research ADAS security technology. The security issues raised by incorporating these technologies into automobiles must be addressed to protect the privacy and safety of passengers and other road users. As a result, the purpose of this research is to investigate the security issues that arise from the integration of ADAS technologies. Addressing these challenges holds the potential to establish a foundation for enhanced safety and dependability within transportation networks amidst the ongoing advancements in vehicle technology. This paper starts by describing the vulnerabilities, threats, assaults, and defense mechanisms of the ADAS. It then delves into the attacks and countermeasures in three categories, namely VANET, Hardware, and Adversarial attacks. VANET attacks encompass threats targeting Vehicular Ad Hoc Networks, aiming to disrupt communication among vehicles or between vehicles and infrastructure. Hardware attacks focus on vulnerabilities within the physical components of ADAS, including sensors, processors, or communication modules. Adversarial attacks involve deliberate manipulations or perturbations introduced into machine learning models or algorithms utilized within ADAS. These attacks aim to deceive or undermine the functionality of AI-based systems, causing misclassification, compromising system integrity, and posing risks to user safety by exploiting vulnerabilities in the AI decision-making process. Finally, this study highlights potential areas for future research, such as the utilization of artificial intelligence (AI), the necessity of industry-wide standardization, and recommends specific future work tailored to each attack described in the corresponding sections.
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spelling doaj.art-3b12c5e046644e16b410d7dae79e4dac2024-01-04T00:02:08ZengIEEEIEEE Access2169-35362024-01-011264367810.1109/ACCESS.2023.334720010373843Securing the Future: A Comprehensive Review of Security Challenges and Solutions in Advanced Driver Assistance SystemsAryan Alpesh Mehta0https://orcid.org/0009-0009-3016-3250Ali Asgar Padaria1https://orcid.org/0009-0006-4616-992XDwij Jayesh Bavisi2https://orcid.org/0000-0002-5041-6720Vijay Ukani3https://orcid.org/0000-0002-7011-6119Priyank Thakkar4https://orcid.org/0000-0001-8241-0617Rebekah Geddam5https://orcid.org/0000-0002-5466-2048Ketan Kotecha6https://orcid.org/0000-0003-2653-3780Ajith Abraham7https://orcid.org/0000-0002-0169-6738Computer Science and Engineering Department, Institute of Technology, Nirma University, Ahmedabad, Gujarat, IndiaComputer Science and Engineering Department, Institute of Technology, Nirma University, Ahmedabad, Gujarat, IndiaComputer Science and Engineering Department, Institute of Technology, Nirma University, Ahmedabad, Gujarat, IndiaComputer Science and Engineering Department, Institute of Technology, Nirma University, Ahmedabad, Gujarat, IndiaComputer Science and Engineering Department, Institute of Technology, Nirma University, Ahmedabad, Gujarat, IndiaComputer Science and Engineering Department, Institute of Technology, Nirma University, Ahmedabad, Gujarat, IndiaSymbiosis Centre for Applied Artificial Intelligence, Symbiosis Institute of Technology, Symbiosis International University, Pune, IndiaSchool of Computer Science Engineering and Technology, Bennett University, Greater Noida, Uttar Pradesh, IndiaAdvanced Driver Assistance Systems (ADAS) are advanced technologies that assist drivers with vehicle operation and navigation. Recent improvements and brisk expansion in the ADAS market, as well as an increase in the frequency of incidents such as sensor spoofing, communication interruption etc., in autonomous vehicles (AVs), have raised the need to research ADAS security technology. The security issues raised by incorporating these technologies into automobiles must be addressed to protect the privacy and safety of passengers and other road users. As a result, the purpose of this research is to investigate the security issues that arise from the integration of ADAS technologies. Addressing these challenges holds the potential to establish a foundation for enhanced safety and dependability within transportation networks amidst the ongoing advancements in vehicle technology. This paper starts by describing the vulnerabilities, threats, assaults, and defense mechanisms of the ADAS. It then delves into the attacks and countermeasures in three categories, namely VANET, Hardware, and Adversarial attacks. VANET attacks encompass threats targeting Vehicular Ad Hoc Networks, aiming to disrupt communication among vehicles or between vehicles and infrastructure. Hardware attacks focus on vulnerabilities within the physical components of ADAS, including sensors, processors, or communication modules. Adversarial attacks involve deliberate manipulations or perturbations introduced into machine learning models or algorithms utilized within ADAS. These attacks aim to deceive or undermine the functionality of AI-based systems, causing misclassification, compromising system integrity, and posing risks to user safety by exploiting vulnerabilities in the AI decision-making process. Finally, this study highlights potential areas for future research, such as the utilization of artificial intelligence (AI), the necessity of industry-wide standardization, and recommends specific future work tailored to each attack described in the corresponding sections.https://ieeexplore.ieee.org/document/10373843/Advanced driver assistance systems (ADAS)attackscountermeasuresdefencessecuritythreats
spellingShingle Aryan Alpesh Mehta
Ali Asgar Padaria
Dwij Jayesh Bavisi
Vijay Ukani
Priyank Thakkar
Rebekah Geddam
Ketan Kotecha
Ajith Abraham
Securing the Future: A Comprehensive Review of Security Challenges and Solutions in Advanced Driver Assistance Systems
IEEE Access
Advanced driver assistance systems (ADAS)
attacks
countermeasures
defences
security
threats
title Securing the Future: A Comprehensive Review of Security Challenges and Solutions in Advanced Driver Assistance Systems
title_full Securing the Future: A Comprehensive Review of Security Challenges and Solutions in Advanced Driver Assistance Systems
title_fullStr Securing the Future: A Comprehensive Review of Security Challenges and Solutions in Advanced Driver Assistance Systems
title_full_unstemmed Securing the Future: A Comprehensive Review of Security Challenges and Solutions in Advanced Driver Assistance Systems
title_short Securing the Future: A Comprehensive Review of Security Challenges and Solutions in Advanced Driver Assistance Systems
title_sort securing the future a comprehensive review of security challenges and solutions in advanced driver assistance systems
topic Advanced driver assistance systems (ADAS)
attacks
countermeasures
defences
security
threats
url https://ieeexplore.ieee.org/document/10373843/
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