Security and Privacy in 5G Internet of Vehicles (IoV) Environment

Osibo, Benjamin Kwapong; Zhang, Chengbo; Xia, Changsen; Zhao, Guanzhe; Jin, Zilong

doi:10.32604/jiot.2021.017943

Cited by 9 publications

(3 citation statements)

References 23 publications

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“…The methods used to address common attacks can be categorized in terms of availability, data integrity, authentication, and confidentiality [ 124 ]. Availability, which refers to the overall network uptime, is a crucial element in IoV.…”

Section: Solutions For Security and Trust In Iovmentioning

confidence: 99%

Security and Trust Management in the Internet of Vehicles (IoV): Challenges and Machine Learning Solutions

Alalwany,

Mahgoub

2024

Sensors

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The Internet of Vehicles (IoV) is a technology that is connected to the public internet and is a subnetwork of the Internet of Things (IoT) in which vehicles with sensors are connected to a mobile and wireless network. Numerous vehicles, users, things, and networks allow nodes to communicate information with their surroundings via various communication channels. IoV aims to enhance the comfort of driving, improve energy management, secure data transmission, and prevent road accidents. Despite IoV’s advantages, it comes with its own set of challenges, particularly in the highly important aspects of security and trust. Trust management is one of the potential security mechanisms aimed at increasing reliability in IoV environments. Protecting IoV environments from diverse attacks poses significant challenges, prompting researchers to explore various technologies for security solutions and trust evaluation methods. Traditional approaches have been employed, but innovative solutions are imperative. Amid these challenges, machine learning (ML) has emerged as a potent solution, leveraging its remarkable advancements to effectively address IoV’s security and trust concerns. ML can potentially be utilized as a powerful technology to address security and trust issues in IoV environments. In this survey, we delve into an overview of IoV and trust management, discussing security requirements, challenges, and attacks. Additionally, we introduce a classification scheme for ML techniques and survey ML-based security and trust management schemes. This research provides an overview for understanding IoV and the potential of ML in improving its security framework. Additionally, it provides insights into the future of trust and security enhancement.

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Section: Solutions For Security and Trust In Iovmentioning

confidence: 99%

Security and Trust Management in the Internet of Vehicles (IoV): Challenges and Machine Learning Solutions

Alalwany,

Mahgoub

2024

Sensors

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“…The network model and algorithmic framework for the IoV consist of the following components [35]- [37]:…”

Section: Algorithm Explanationmentioning

confidence: 99%

Enhancing the Internet of Vehicles through Collaborative Cryptographic Mechanisms with IRS

Aljumaili,

Trabelsi,

Jerbi

et al. 2024

icisna

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The Internet of Vehicles (IoV) is transforming transportation systems by allowing for seamless communication and collaboration between vehicles, roadside units (RSUs), and cloud servers. However, the dynamic and varied character of IoV settings raises serious security and efficiency concerns. This work suggests a novel strategy to addressing these issues by combining collaborative cryptographic processes and intelligent reflecting surfaces (IRS). Our method uses modern encryption techniques, such as the modern Encryption Standard (AES), to assure safe data delivery, while intelligent reflecting surfaces dynamically modify their reflection qualities to improve signal propagation and reception. We provide a detailed network model and algorithmic framework for implementing the suggested strategy, with a focus on cryptographic techniques and the role of intelligent reflecting surfaces in improving communication security and efficiency. Through theoretical analysis and debate, we emphasize the possible benefits of incorporating intelligent reflecting surfaces in IoV networks, such as improved network coverage, lower communication costs, and increased energy efficiency. Furthermore, we explore the implications of our strategy for IoV security and suggest future research areas in this area.

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“…Given the diverse sources of data collection and dissemination in IoV-from traffic management systems to third-party service providers-users must have clear and manageable controls over who accesses their data and for what purpose. This aspect is particularly challenging in IoV due to the interconnected nature of vehicular networks and the range of stakeholders involved [12]. Moreover, when data sharing is necessary for the functionality of IoV services, its execution requires robust security measures.…”

Section: Privacy Requirements In Iov Networkmentioning

confidence: 99%

Federated Learning for Intrusion Detection Systems in Internet of Vehicles: A General Taxonomy, Applications, and Future Directions

Alsamiri,

Alsubhi

2023

Future Internet

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In recent years, the Internet of Vehicles (IoV) has garnered significant attention from researchers and automotive industry professionals due to its expanding range of applications and services aimed at enhancing road safety and driver/passenger comfort. However, the massive amount of data spread across this network makes securing it challenging. The IoV network generates, collects, and processes vast amounts of valuable and sensitive data that intruders can manipulate. An intrusion detection system (IDS) is the most typical method to protect such networks. An IDS monitors activity on the road to detect any sign of a security threat and generates an alert if a security anomaly is detected. Applying machine learning methods to large datasets helps detect anomalies, which can be utilized to discover potential intrusions. However, traditional centralized learning algorithms require gathering data from end devices and centralizing it for training on a single device. Vehicle makers and owners may not readily share the sensitive data necessary for training the models. Granting a single device access to enormous volumes of personal information raises significant privacy concerns, as any system-related problems could result in massive data leaks. To alleviate these problems, more secure options, such as Federated Learning (FL), must be explored. A decentralized machine learning technique, FL allows model training on client devices while maintaining user data privacy. Although FL for IDS has made significant progress, to our knowledge, there has been no comprehensive survey specifically dedicated to exploring the applications of FL for IDS in the IoV environment, similar to successful systems research in deep learning. To address this gap, we undertake a well-organized literature review on IDSs based on FL in an IoV environment. We introduce a general taxonomy to describe the FL systems to ensure a coherent structure and guide future research. Additionally, we identify the relevant state of the art in FL-based intrusion detection within the IoV domain, covering the years from FL’s inception in 2016 through 2023. Finally, we identify challenges and future research directions based on the existing literature.

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Security and Privacy in 5G Internet of Vehicles (IoV) Environment

Cited by 9 publications

References 23 publications

Security and Trust Management in the Internet of Vehicles (IoV): Challenges and Machine Learning Solutions

Security and Trust Management in the Internet of Vehicles (IoV): Challenges and Machine Learning Solutions

Enhancing the Internet of Vehicles through Collaborative Cryptographic Mechanisms with IRS

Federated Learning for Intrusion Detection Systems in Internet of Vehicles: A General Taxonomy, Applications, and Future Directions

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