Towards a Cybersecurity Testbed for Agricultural Vehicles and Environments

• URL: http://arxiv.org/abs/2205.05866v1
• Date: Thu, 12 May 2022 03:27:06 GMT
• Title: Towards a Cybersecurity Testbed for Agricultural Vehicles and Environments
• Authors: Mark Freyhof and George Grispos and Santosh Pitla and Cody Stolle
• Abstract summary: An increasing number of agricultural systems and vehicles are connected to the Internet. Previous research has focused on general cybersecurity concerns in the farming and agricultural industries. This paper presents STAVE - a Security Testbed for Agricultural Vehicles and Environments.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In today's modern farm, an increasing number of agricultural systems and vehicles are connected to the Internet. While the benefits of networked agricultural machinery are attractive, this technological shift is also creating an environment that is conducive to cyberattacks. While previous research has focused on general cybersecurity concerns in the farming and agricultural industries, minimal research has focused on techniques for identifying security vulnerabilities within actual agricultural systems that could be exploited by cybercriminals. Hence, this paper presents STAVE - a Security Testbed for Agricultural Vehicles and Environments - as a potential solution to assist with the identification of cybersecurity vulnerabilities within commercially available off-the-shelf components used in certain agricultural systems. This paper reports ongoing research efforts to develop and refine the STAVE testbed, along with describing initial cybersecurity experimentation which aims to identify security vulnerabilities within wireless and Controller Area Network (CAN) Bus agricultural vehicle components.

Related papers

• A Survey on the Application of Generative Adversarial Networks in Cybersecurity: Prospective, Direction and Open Research Scopes [1.3631461603291568]
  Generative Adversarial Networks (GANs) have emerged as powerful solutions for addressing the constantly changing security issues. This survey studies the significance of the deep learning model, precisely on GANs, in strengthening cybersecurity defenses. The focus is to examine how GANs can be influential tools to strengthen cybersecurity defenses in these domains.
  arXiv  Detail & Related papers  (2024-07-11T19:51:48Z)
• Navigating the road to automotive cybersecurity compliance [39.79758414095764]
  The automotive industry is compelled to adopt robust cybersecurity measures to safeguard both vehicles and data against potential threats. The future of automotive cybersecurity lies in the continuous development of advanced protective measures and collaborative efforts among all stakeholders.
  arXiv  Detail & Related papers  (2024-06-29T16:07:48Z)
• Cybersecurity Pathways Towards CE-Certified Autonomous Forestry Machines [0.1396038187727205]
  We identify challenges towards CE-certified autonomous forestry machines focusing on cybersecurity and safety. We discuss the relationship between safety and cybersecurity risk assessment and their relation to AI.
  arXiv  Detail & Related papers  (2024-04-30T15:44:57Z)
• A Review of Cybersecurity Incidents in the Food and Agriculture Sector [2.0358239640633737]
  This manuscript reviews disclosed and documented cybersecurity incidents in the Food & Agriculture (FA) sector. Thirty cybersecurity incidents were identified, which took place between July 2011 and April 2023. The need for AI assurance in the FA sector is explained, and the Farmer-Centered AI (FCAI) framework is proposed.
  arXiv  Detail & Related papers  (2024-03-12T19:15:20Z)
• 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)
• A Survey and Comparative Analysis of Security Properties of CAN Authentication Protocols [92.81385447582882]
  The Controller Area Network (CAN) bus leaves in-vehicle communications inherently non-secure. This paper reviews and compares the 15 most prominent authentication protocols for the CAN bus. We evaluate protocols based on essential operational criteria that contribute to ease of implementation.
  arXiv  Detail & Related papers  (2024-01-19T14:52:04Z)
• Empowering Agrifood System with Artificial Intelligence: A Survey of the Progress, Challenges and Opportunities [86.89427012495457]
  We review how AI techniques can transform agrifood systems and contribute to the modern agrifood industry. We present a progress review of AI methods in agrifood systems, specifically in agriculture, animal husbandry, and fishery. We highlight potential challenges and promising research opportunities for transforming modern agrifood systems with AI.
  arXiv  Detail & Related papers  (2023-05-03T05:16:54Z)
• Security and Safety Aspects of AI in Industry Applications [0.0]
  We summarise issues in the domains of safety and security in machine learning that will affect industry sectors in the next five to ten years. Reports of underlying problems in both safety and security related domains, for instance adversarial attacks have unsettled early adopters. The problem for real-world applicability lies in being able to assess the risk of applying these technologies.
  arXiv  Detail & Related papers  (2022-07-16T16:41:00Z)
• Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety [54.478842696269304]
  The use of deep neural networks (DNNs) in safety-critical applications is challenging due to numerous model-inherent shortcomings. In recent years, a zoo of state-of-the-art techniques aiming to address these safety concerns has emerged. Our paper addresses both machine learning experts and safety engineers.
  arXiv  Detail & Related papers  (2021-04-29T09:54:54Z)
• Dos and Don'ts of Machine Learning in Computer Security [74.1816306998445]
  Despite great potential, machine learning in security is prone to subtle pitfalls that undermine its performance. We identify common pitfalls in the design, implementation, and evaluation of learning-based security systems. We propose actionable recommendations to support researchers in avoiding or mitigating the pitfalls where possible.
  arXiv  Detail & Related papers  (2020-10-19T13:09:31Z)
• 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.