Risk Assessment and Threat Modeling for safe autonomous driving technology
- URL: http://arxiv.org/abs/2505.02231v1
- Date: Sun, 04 May 2025 19:51:06 GMT
- Title: Risk Assessment and Threat Modeling for safe autonomous driving technology
- Authors: Ian Alexis Wong Paz, Anuvinda Balan, Sebastian Campos, Ehud Orenstain, Sudip Dhakal,
- Abstract summary: This research endeavors to develop a comprehensive threat model for AV systems.<n>It categorizes threats into Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service (DoS), and Elevation of Privilege.<n>A systematic risk assessment is conducted to evaluate vulnerabilities across various AV components.
- Score: 0.0
- License: http://creativecommons.org/publicdomain/zero/1.0/
- Abstract: This research paper delves into the field of autonomous vehicle technology, examining the vulnerabilities inherent in each component of these transformative vehicles. Autonomous vehicles (AVs) are revolutionizing transportation by seamlessly integrating advanced functionalities such as sensing, perception, planning, decision-making, and control. However, their reliance on interconnected systems and external communication interfaces renders them susceptible to cybersecurity threats. This research endeavors to develop a comprehensive threat model for AV systems, employing OWASP Threat Dragon and the STRIDE framework. This model categorizes threats into Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service (DoS), and Elevation of Privilege. A systematic risk assessment is conducted to evaluate vulnerabilities across various AV components, including perception modules, planning systems, control units, and communication interfaces.
Related papers
- A Survey of Model Extraction Attacks and Defenses in Distributed Computing Environments [55.60375624503877]
Model Extraction Attacks (MEAs) threaten modern machine learning systems by enabling adversaries to steal models, exposing intellectual property and training data.<n>This survey is motivated by the urgent need to understand how the unique characteristics of cloud, edge, and federated deployments shape attack vectors and defense requirements.<n>We systematically examine the evolution of attack methodologies and defense mechanisms across these environments, demonstrating how environmental factors influence security strategies in critical sectors such as autonomous vehicles, healthcare, and financial services.
arXiv Detail & Related papers (2025-02-22T03:46:50Z) - ACTISM: Threat-informed Dynamic Security Modelling for Automotive Systems [7.3347982474177185]
ACTISM (Automotive Consequence-Driven and Threat-Informed Security Modelling) is an integrated security modelling framework.<n>It enhances the resilience of automotive systems by dynamically updating their cybersecurity posture.<n>We demonstrate the effectiveness of ACTISM by applying it to a real-world example of the Tesla Electric Vehicle's In-Vehicle Infotainment system.<n>We report the results of a practitioners' survey on the usefulness of ACTISM and its future directions.
arXiv Detail & Related papers (2024-11-30T09:58:48Z) - A Survey on Adversarial Robustness of LiDAR-based Machine Learning Perception in Autonomous Vehicles [0.0]
This survey focuses on the intersection of Adversarial Machine Learning (AML) and autonomous systems.
We comprehensively explore the threat landscape, encompassing cyber-attacks on sensors and adversarial perturbations.
This paper endeavors to present a concise overview of the challenges and advances in securing autonomous driving systems against adversarial threats.
arXiv Detail & Related papers (2024-11-21T01:26:52Z) - 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) - CANEDERLI: On The Impact of Adversarial Training and Transferability on CAN Intrusion Detection Systems [17.351539765989433]
A growing integration of vehicles with external networks has led to a surge in attacks targeting their Controller Area Network (CAN) internal bus.
As a countermeasure, various Intrusion Detection Systems (IDSs) have been suggested in the literature to prevent and mitigate these threats.
Most of these systems rely on data-driven approaches such as Machine Learning (ML) and Deep Learning (DL) models.
In this paper, we present CANEDERLI, a novel framework for securing CAN-based IDSs.
arXiv Detail & Related papers (2024-04-06T14:54:11Z) - Testing autonomous vehicles and AI: perspectives and challenges from cybersecurity, transparency, robustness and fairness [53.91018508439669]
The study explores the complexities of integrating Artificial Intelligence into Autonomous Vehicles (AVs)
It examines the challenges introduced by AI components and the impact on testing procedures.
The paper identifies significant challenges and suggests future directions for research and development of AI in AV technology.
arXiv Detail & Related papers (2024-02-21T08:29:42Z) - Autonomous Vehicles an overview on system, cyber security, risks,
issues, and a way forward [0.0]
This chapter explores the complex realm of autonomous cars, analyzing their fundamental components and operational characteristics.
The primary focus of this investigation lies in the realm of cybersecurity, specifically in the context of autonomous vehicles.
A comprehensive analysis will be conducted to explore various risk management solutions aimed at protecting these vehicles from potential threats.
arXiv Detail & Related papers (2023-09-25T15:19:09Z) - When Authentication Is Not Enough: On the Security of Behavioral-Based Driver Authentication Systems [53.2306792009435]
We develop two lightweight driver authentication systems based on Random Forest and Recurrent Neural Network architectures.
We are the first to propose attacks against these systems by developing two novel evasion attacks, SMARTCAN and GANCAN.
Through our contributions, we aid practitioners in safely adopting these systems, help reduce car thefts, and enhance driver security.
arXiv Detail & Related papers (2023-06-09T14:33:26Z) - Deep Learning-Based Autonomous Driving Systems: A Survey of Attacks and
Defenses [13.161104978510943]
This survey provides a thorough analysis of different attacks that may jeopardize autonomous driving systems.
It covers adversarial attacks for various deep learning models and attacks in both physical and cyber context.
Some promising research directions are suggested in order to improve deep learning-based autonomous driving safety.
arXiv Detail & Related papers (2021-04-05T06:31:47Z) - Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks,
and Defenses [150.64470864162556]
This work systematically categorizes and discusses a wide range of dataset vulnerabilities and exploits.
In addition to describing various poisoning and backdoor threat models and the relationships among them, we develop their unified taxonomy.
arXiv Detail & Related papers (2020-12-18T22:38:47Z) - Towards robust sensing for Autonomous Vehicles: An adversarial
perspective [82.83630604517249]
It is of primary importance that the resulting decisions are robust to perturbations.
Adversarial perturbations are purposefully crafted alterations of the environment or of the sensory measurements.
A careful evaluation of the vulnerabilities of their sensing system(s) is necessary in order to build and deploy safer systems.
arXiv Detail & Related papers (2020-07-14T05:25:15Z)
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.