Security threat detection and mitigation in autonomous vehicles
Haque, AKM Mahfuzul (2024)
Diplomityö
2024
School of Engineering Science, Tietotekniikka
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe2025050838623
https://urn.fi/URN:NBN:fi-fe2025050838623
Tiivistelmä
This study explores the security challenges and mitigation strategies for autonomous vehicles (AVs) and connected autonomous vehicles (CAVs). As AV technology rapidly evolves, which makes ensuring the safety and integrity of these systems paramount. This study examines and aggregates peer-reviewed literature published between 2019 and 2024, covering the cybersecurity issues, threats, and deterrent measures related to AVs and CAVs. Through the review of literature from online databases which include ACM Digital Library, IEEE Xplore, Scopus, and WILEY Online Library, the paper identifies key vulnerabilities in sensor data, communication protocols, decision-making algorithms.
This research identifies data integrity manipulation, system tampering, and cyberattacks on vehicle-to-everything (V2X) communication. It also evaluates how these vulnerabilities affect passenger safety and the reliability of AV systems. Real-world situations also identify some countermeasures such as advanced encryption techniques and intrusion detection systems with threat detection based on machine learning as of crucial effectiveness. This literature shows the use of robust hardware and software combinations to mitigate risk control, safe network structure, and integration.
At the same time, this paper finds some of the most important areas that previous research has missed out. A case in point is that there are no datasets for testing security solutions on which you can rely, and very little attention has been given to threats regardless of whether they emerge in the ecosystem of AV. Additional research in this area should take on board both academic research and policy. Ultimately, this report underlines the need for a globally proactive and comprehensive approach to ensure that autonomous driving systems can withstand a challenge from the rapidly evolving security threat landscape.
This research identifies data integrity manipulation, system tampering, and cyberattacks on vehicle-to-everything (V2X) communication. It also evaluates how these vulnerabilities affect passenger safety and the reliability of AV systems. Real-world situations also identify some countermeasures such as advanced encryption techniques and intrusion detection systems with threat detection based on machine learning as of crucial effectiveness. This literature shows the use of robust hardware and software combinations to mitigate risk control, safe network structure, and integration.
At the same time, this paper finds some of the most important areas that previous research has missed out. A case in point is that there are no datasets for testing security solutions on which you can rely, and very little attention has been given to threats regardless of whether they emerge in the ecosystem of AV. Additional research in this area should take on board both academic research and policy. Ultimately, this report underlines the need for a globally proactive and comprehensive approach to ensure that autonomous driving systems can withstand a challenge from the rapidly evolving security threat landscape.