Survey on Security Attacks in Connected and Autonomous Vehicular Systems
- URL: http://arxiv.org/abs/2310.09510v1
- Date: Sat, 14 Oct 2023 06:37:05 GMT
- Title: Survey on Security Attacks in Connected and Autonomous Vehicular Systems
- Authors: S M Mostaq Hossain, Shampa Banik, Trapa Banik, Ashfak Md Shibli,
- Abstract summary: This study provides a brief review of the state of cyber security in the CAVs environment.
It categorizes the multiple cybersecurity threats and weaknesses in the context of CAVs into three groups: attacks on the vehicles network, attacks on the Internet at large, and other attacks.
It details the most uptodate defense tactics for securing CAVs and analyzes how effective they are.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Connected and autonomous vehicles, also known as CAVs, are a general trend in the evolution of the automotive industry that can be utilized to make transportation safer, improve the number of mobility options available, user costs will go down and new jobs will be created. However, as our society grows more automated and networked, criminal actors will have additional opportunities to conduct a variety of attacks, putting CAV security in danger. By providing a brief review of the state of cyber security in the CAVs environment, this study aims to draw attention to the issues and concerns associated with security. The first thing it does is categorize the multiple cybersecurity threats and weaknesses in the context of CAVs into three groups: attacks on the vehicles network, attacks on the Internet at large, and other attacks. This is done in accordance with the various communication networks and targets under attack. Next, it considers the possibility of cyber attacks to be an additional form of threat posed by the environment of CAVs. After that, it details the most uptodate defense tactics for securing CAVs and analyzes how effective they are. In addition, it draws some conclusions about the various cyber security and safety requirements of CAVs that are now available, which is beneficial for the use of CAVs in the real world. At the end, we discussed some implications on Adversary Attacks on Autonomous Vehicles. In conclusion, a number of difficulties and unsolved issues for future research are analyzed and explored.
Related papers
- Safeguarding connected autonomous vehicle communication: Protocols, intra- and inter-vehicular attacks and defenses [30.18378702161015]
This paper contributes by presenting a detailed analysis of existing security frameworks and protocols.
We propose a set of best practices for enhancing CAV communication security.
Key contributions include the development of a new classification system for CAV security threats.
arXiv Detail & Related papers (2025-02-06T16:43:23Z) - Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving [65.61999354218628]
We take the first step toward designing black-box adversarial attacks specifically targeting vision-language models (VLMs) in autonomous driving systems.
We propose Cascading Adversarial Disruption (CAD), which targets low-level reasoning breakdown by generating and injecting semantics.
We present Risky Scene Induction, which addresses dynamic adaptation by leveraging a surrogate VLM to understand and construct high-level risky scenarios.
arXiv Detail & Related papers (2025-01-23T11:10:02Z) - Security by Design Issues in Autonomous Vehicles [0.7999703756441756]
This research outlines the diverse security layers, spanning physical, cyber, coding, and communication aspects, in the context of AVs.
We provide insights into potential solutions for each potential attack vector, ensuring that autonomous vehicles remain secure and resilient in an evolving threat landscape.
arXiv Detail & Related papers (2025-01-07T19:24:11Z) - Collaborative Approaches to Enhancing Smart Vehicle Cybersecurity by AI-Driven Threat Detection [0.0]
The automotive industry increasingly adopts connected and automated vehicles (CAVs)
With the emergence of new vulnerabilities and security requirements, the integration of advanced technologies presents promising avenues for enhancing CAV cybersecurity.
The roadmap for cybersecurity in autonomous vehicles emphasizes the importance of efficient intrusion detection systems and AI-based techniques.
arXiv Detail & Related papers (2024-12-31T04:08:42Z) - Work-in-Progress: Crash Course: Can (Under Attack) Autonomous Driving Beat Human Drivers? [60.51287814584477]
This paper evaluates the inherent risks in autonomous driving by examining the current landscape of AVs.
We develop specific claims highlighting the delicate balance between the advantages of AVs and potential security challenges in real-world scenarios.
arXiv Detail & Related papers (2024-05-14T09:42:21Z) - Differentiated Security Architecture for Secure and Efficient Infotainment Data Communication in IoV Networks [55.340315838742015]
Negligence on the security of infotainment data communication in IoV networks can unintentionally open an easy access point for social engineering attacks.
In particular, we first classify data communication in the IoV network, examine the security focus of each data communication, and then develop a differentiated security architecture to provide security protection on a file-to-file basis.
arXiv Detail & Related papers (2024-03-29T12:01:31Z) - A Survey of Security in UAVs and FANETs: Issues, Threats, Analysis of Attacks, and Solutions [1.0923877073891446]
It is critical that security is ensured for UAVs and the networks that provide communication between UAVs.
This survey seeks to provide a comprehensive perspective on security within the domain of UAVs and Flying Ad Hoc Networks (FANETs)
arXiv Detail & Related papers (2023-06-25T16:15:40Z) - Reinforcement Learning based Cyberattack Model for Adaptive Traffic
Signal Controller in Connected Transportation Systems [61.39400591328625]
In a connected transportation system, adaptive traffic signal controllers (ATSC) utilize real-time vehicle trajectory data received from vehicles to regulate green time.
This wirelessly connected ATSC increases cyber-attack surfaces and increases their vulnerability to various cyber-attack modes.
One such mode is a'sybil' attack in which an attacker creates fake vehicles in the network.
An RL agent is trained to learn an optimal rate of sybil vehicle injection to create congestion for an approach(s)
arXiv Detail & Related papers (2022-10-31T20:12:17Z) - Simulating Malicious Attacks on VANETs for Connected and Autonomous
Vehicle Cybersecurity: A Machine Learning Dataset [0.4129225533930965]
Connected and Autonomous Vehicles (CAVs) rely on Vehicular Adhoc Networks with wireless communication between vehicles and roadside infrastructure to support safe operation.
cybersecurity attacks pose a threat to VANETs and the safe operation of CAVs.
This study proposes the use of simulation for modelling typical communication scenarios which may be subject to malicious attacks.
arXiv Detail & Related papers (2022-02-15T20:08:58Z) - Fixed Points in Cyber Space: Rethinking Optimal Evasion Attacks in the
Age of AI-NIDS [70.60975663021952]
We study blackbox adversarial attacks on network classifiers.
We argue that attacker-defender fixed points are themselves general-sum games with complex phase transitions.
We show that a continual learning approach is required to study attacker-defender dynamics.
arXiv Detail & Related papers (2021-11-23T23:42:16Z) - Adversarial Machine Learning Attacks and Defense Methods in the Cyber
Security Domain [58.30296637276011]
This paper summarizes the latest research on adversarial attacks against security solutions based on machine learning techniques.
It is the first to discuss the unique challenges of implementing end-to-end adversarial attacks in the cyber security domain.
arXiv Detail & Related papers (2020-07-05T18:22:40Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.