PASSION: Permissioned Access Control for Segmented Devices and Identity for IoT Networks

• URL: http://arxiv.org/abs/2310.05032v1
• Date: Sun, 8 Oct 2023 06:28:32 GMT
• Title: PASSION: Permissioned Access Control for Segmented Devices and Identity for IoT Networks
• Authors: Hisham Ali, Mwrwan Abubakar, Jawad Ahmad, William J. Buchanan, Zakwan Jaroucheh,
• Abstract summary: This paper introduces a privacy-preserving method in the industry's IoT systems using blockchain-based data access control. It maintains event information confidentiality, integrity and authenticity.
• Score: 0.5991851254194097
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In recent years, there has been a significant proliferation of industrial Internet of Things (IoT) applications, with a wide variety of use cases being developed and put into operation. As the industrial IoT landscape expands, the establishment of secure and reliable infrastructure becomes crucial to instil trust among users and stakeholders, particularly in addressing fundamental concerns such as traceability, integrity protection, and privacy that some industries still encounter today. This paper introduces a privacy-preserving method in the industry's IoT systems using blockchain-based data access control for remote industry safety monitoring and maintaining event information confidentiality, integrity and authenticity.

Related papers

• Intelligent Detection of Non-Essential IoT Traffic on the Home Gateway [45.70482328441101]
  This work presents ML-IoTrim, a system for detecting and mitigating non-essential IoT traffic by analyzing network behavior at the edge. We test our framework in a consumer smart home setup with IoT devices from five categories, demonstrating that the model can accurately identify and block non-essential traffic. This research advances privacy-aware traffic control in smart homes, paving the way for future developments in IoT device privacy.
  arXiv  Detail & Related papers  (2025-04-22T09:40:05Z)
• ACRIC: Securing Legacy Communication Networks via Authenticated Cyclic Redundancy Integrity Check [98.34702864029796]
  Recent security incidents in safety-critical industries exposed how the lack of proper message authentication enables attackers to inject malicious commands or alter system behavior.<n>These shortcomings have prompted new regulations that emphasize the pressing need to strengthen cybersecurity.<n>We introduce ACRIC, a message authentication solution to secure legacy industrial communications.
  arXiv  Detail & Related papers  (2024-11-21T18:26:05Z)
• Cyberattack Data Analysis in IoT Environments using Big Data [0.0]
  Our research addresses the growing connectivity and security challenges, including interoperability and standardized protocols. Our in-depth analysis of security vulnerabilities identified intricate patterns and threats, such as attack behavior, network traffic anomalies, TCP flag usage, and targeted attacks.
  arXiv  Detail & Related papers  (2024-06-14T02:12:43Z)
• IoT in the Cloud: Exploring Security Challenges and Mitigations for a Connected World [18.36339203254509]
  The Internet of Things (IoT) has seen remarkable advancements in recent years, leading to a paradigm shift in the digital landscape. IoT devices, inherently connected to the internet, are susceptible to various forms of attacks. IoT services often handle sensitive user data, which could be exploited by malicious actors or unauthorized service providers.
  arXiv  Detail & Related papers  (2024-02-01T05:55:43Z)
• Classification of cyber attacks on IoT and ubiquitous computing devices [49.1574468325115]
  This paper provides a classification of IoT malware. Major targets and used exploits for attacks are identified and referred to the specific malware. The majority of current IoT attacks continue to be of comparably low effort and level of sophistication and could be mitigated by existing technical measures.
  arXiv  Detail & Related papers  (2023-12-01T16:10:43Z)
• Towards Artificial General Intelligence (AGI) in the Internet of Things (IoT): Opportunities and Challenges [55.82853124625841]
  Artificial General Intelligence (AGI) possesses the capacity to comprehend, learn, and execute tasks with human cognitive abilities. This research embarks on an exploration of the opportunities and challenges towards achieving AGI in the context of the Internet of Things. The application spectrum for AGI-infused IoT is broad, encompassing domains ranging from smart grids, residential environments, manufacturing, and transportation to environmental monitoring, agriculture, healthcare, and education.
  arXiv  Detail & Related papers  (2023-09-14T05:43:36Z)
• Navigating the IoT landscape: Unraveling forensics, security issues, applications, research challenges, and future [6.422895251217666]
  This paper reviews forensic and security issues associated with IoT in different fields. Most IoT devices are vulnerable to attacks due to a lack of standardized security measures. To fulfil the security-conscious needs of consumers, IoT can be used to develop a smart home system.
  arXiv  Detail & Related papers  (2023-09-06T04:41:48Z)
• FedBlockHealth: A Synergistic Approach to Privacy and Security in IoT-Enabled Healthcare through Federated Learning and Blockchain [2.993954417409032]
  The rapid adoption of Internet of Things (IoT) devices in healthcare has introduced new challenges in preserving data privacy, security and patient safety. Traditional approaches need to ensure security and privacy while maintaining computational efficiency. This paper proposes a novel hybrid approach combining federated learning and blockchain technology to provide a secure and privacy-preserved solution.
  arXiv  Detail & Related papers  (2023-04-16T01:55:31Z)
• Federated Learning for Industrial Internet of Things in Future Industries [106.13524161081355]
  The Industrial Internet of Things (IIoT) offers promising opportunities to transform the operation of industrial systems. Recently, artificial intelligence (AI) has been widely utilized for realizing intelligent IIoT applications. Federated Learning (FL) is particularly attractive for intelligent IIoT networks by coordinating multiple IIoT devices and machines to perform AI training at the network edge.
  arXiv  Detail & Related papers  (2021-05-31T01:02:59Z)
• A Detail Study of Security and Privacy issues of Internet of Things [0.7874708385247353]
  The Internet of Things refers to the billions of physical objects around the planet that are now connected to the Internet. The main focus of this chapter is to systematically review the security and privacy of the Internet of Things in the present world.
  arXiv  Detail & Related papers  (2020-09-14T11:58:22Z)
• Trustworthy AI Inference Systems: An Industry Research View [58.000323504158054]
  We provide an industry research view for approaching the design, deployment, and operation of trustworthy AI inference systems. We highlight opportunities and challenges in AI systems using trusted execution environments. We outline areas of further development that require the global collective attention of industry, academia, and government researchers.
  arXiv  Detail & Related papers  (2020-08-10T23:05:55Z)
• Smart Home, security concerns of IoT [91.3755431537592]
  The IoT (Internet of Things) has become widely popular in the domestic environments. People are renewing their homes into smart homes; however, the privacy concerns of owning many Internet connected devices with always-on environmental sensors remain insufficiently addressed. Default and weak passwords, cheap materials and hardware, and unencrypted communication are identified as the principal threats and vulnerabilities of IoT devices.
  arXiv  Detail & Related papers  (2020-07-06T10:36:11Z)
• Beyond privacy regulations: an ethical approach to data usage in transportation [64.86110095869176]
  We describe how Federated Machine Learning can be applied to the transportation sector. We see Federated Learning as a method that enables us to process privacy-sensitive data, while respecting customer's privacy.
  arXiv  Detail & Related papers  (2020-04-01T15:10:12Z)

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.