Differentiated Security Architecture for Secure and Efficient Infotainment Data Communication in IoV Networks

• URL: http://arxiv.org/abs/2403.20136v1
• Date: Fri, 29 Mar 2024 12:01:31 GMT
• Title: Differentiated Security Architecture for Secure and Efficient Infotainment Data Communication in IoV Networks
• Authors: Jiani Fan, Lwin Khin Shar, Jiale Guo, Wenzhuo Yang, Dusit Niyato, Kwok-Yan Lam,
• Abstract summary: 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.
• Score: 55.340315838742015
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This paper aims to provide differentiated security protection for infotainment data communication in Internet-of-Vehicle (IoV) networks. The IoV is a network of vehicles that uses various sensors, software, built-in hardware, and communication technologies to enable information exchange between pedestrians, cars, and urban infrastructure. Negligence on the security of infotainment data communication in IoV networks can unintentionally open an easy access point for social engineering attacks. The attacker can spread false information about traffic conditions, mislead drivers in their directions, and interfere with traffic management. Such attacks can also cause distractions to the driver, which has a potential implication for the safety of driving. The existing literature on IoV communication and network security focuses mainly on generic solutions. In a heterogeneous communication network where different types of communication coexist, we can improve the efficiency of security solutions by considering the different security and efficiency requirements of data communications. Hence, we propose a differentiated security mechanism for protecting infotainment data communication in IoV networks. 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. Our architecture leverages Named Data Networking (NDN) so that infotainment files can be efficiently circulated throughout the network where any node can own a copy of the file, thus improving the hit ratio for user file requests. In addition, we propose a time-sensitive Key-Policy Attribute-Based Encryption (KP-ABE) scheme for sharing subscription-based infotainment data...

Related papers

• Unique ID based Trust Scheme for Improved IoV Wireless Sensor Network Security Against Power Controlled Sybil Attacks [1.906179410714637]
  Wireless sensor networks (WSNs) are widely used in vehicular networks to support Vehicle-to-Everything (V2X) communications. WSNs face security challenges due to their distributed nature and resource limited modules. This paper proposes a unique identification based trust path routing scheme (UITrust) to avoid Sybil attacks.
  arXiv  Detail & Related papers  (2024-10-05T07:20:55Z)
• 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)
• AI-Protected Blockchain-based IoT environments: Harnessing the Future of Network Security and Privacy [0.0]
  This paper explores the pivotal role of artificial intelligence in bolstering blockchain-enabled IoT systems. When coupled with AI, these systems gain the ability to not only automate and optimize security protocols but also respond to new and evolving cyber threats. This paper aims to provide an in-depth understanding of how AI-enhanced blockchain technology can revolutionize network security and privacy in IoT environments.
  arXiv  Detail & Related papers  (2024-05-22T17:14:19Z)
• Decentralized Multimedia Data Sharing in IoV: A Learning-based Equilibrium of Supply and Demand [57.82021900505197]
  Internet of Vehicles (IoV) has great potential to transform transportation systems by enhancing road safety, reducing traffic congestion, and improving user experience through onboard infotainment applications. Decentralized data sharing can improve security, privacy, reliability, and facilitate infotainment data sharing in IoVs. We propose a decentralized data-sharing incentive mechanism based on multi-intelligent reinforcement learning to learn the supply-demand balance in markets.
  arXiv  Detail & Related papers  (2024-03-29T14:58:28Z)
• SISSA: Real-time Monitoring of Hardware Functional Safety and Cybersecurity with In-vehicle SOME/IP Ethernet Traffic [49.549771439609046]
  We propose SISSA, a SOME/IP communication traffic-based approach for modeling and analyzing in-vehicle functional safety and cyber security. Specifically, SISSA models hardware failures with the Weibull distribution and addresses five potential attacks on SOME/IP communication. Extensive experimental results show the effectiveness and efficiency of SISSA.
  arXiv  Detail & Related papers  (2024-02-21T03:31:40Z)
• The Security and Privacy of Mobile Edge Computing: An Artificial Intelligence Perspective [64.36680481458868]
  Mobile Edge Computing (MEC) is a new computing paradigm that enables cloud computing and information technology (IT) services to be delivered at the network's edge. This paper provides a survey of security and privacy in MEC from the perspective of Artificial Intelligence (AI) We focus on new security and privacy issues, as well as potential solutions from the viewpoints of AI.
  arXiv  Detail & Related papers  (2024-01-03T07:47:22Z)
• Blockchain-aided Secure Semantic Communication for AI-Generated Content in Metaverse [59.04428659123127]
  We propose a blockchain-aided semantic communication framework for AIGC services in virtual transportation networks. We illustrate a training-based semantic attack scheme to generate adversarial semantic data by various loss functions. We also design a semantic defense scheme that uses the blockchain and zero-knowledge proofs to tell the difference between the semantic similarities of adversarial and authentic semantic data.
  arXiv  Detail & Related papers  (2023-01-25T02:32:02Z)
• Efficient Federated Learning with Spike Neural Networks for Traffic Sign Recognition [70.306089187104]
  We introduce powerful Spike Neural Networks (SNNs) into traffic sign recognition for energy-efficient and fast model training. Numerical results indicate that the proposed federated SNN outperforms traditional federated convolutional neural networks in terms of accuracy, noise immunity, and energy efficiency as well.
  arXiv  Detail & Related papers  (2022-05-28T03:11:48Z)
• HBFL: A Hierarchical Blockchain-based Federated Learning Framework for a Collaborative IoT Intrusion Detection [0.0]
  We propose a hierarchical blockchain-based federated learning framework to enable secure and privacy-preserved collaborative IoT intrusion detection. The proposed ML-based intrusion detection framework follows a hierarchical federated learning architecture to ensure the privacy of the learning process and organisational data. The outcome is a securely designed ML-based intrusion detection system capable of detecting a wide range of malicious activities while preserving data privacy.
  arXiv  Detail & Related papers  (2022-04-08T19:06:16Z)
• A Systematic Literature Review on Blockchain Enabled Federated Learning Framework for Internet of Vehicles [1.0499611180329804]
  Federated Learning (FL) has been proven as an emerging idea to protect IoVs data privacy and security. We present a comprehensive survey on the application and implementation of BC-Enabled Learning frameworks for IoVs.
  arXiv  Detail & Related papers  (2022-03-10T07:06:04Z)

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.