Security Threats &amp; Attacks in IoV Environment: Open Research Issues and Challenges

Garg, Ayush; Chauhan, Aditya; Shambharkar, Prashant Giridhar

doi:10.1109/icicict54557.2022.9917816

Cited by 4 publications

(3 citation statements)

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“…In [ 72 ], the security challenges within vehicular networks are discussed primarily, with an emphasis on attacks and preventive measures, but without explicit mentions of machine learning or trust. In [ 73 ], the author examined the security challenges of IoV environments, emphasizing various attack types and the need for privacy preservation and strong authentication, thereby contributing to discussions on security but without ML techniques.…”

Section: Related Surveysmentioning

confidence: 99%

“…We provide a comprehensive discussion of related surveys, the difference between our survey and these related surveys, as well as the survey’s main focus, which is shown in Figure 2 . Despite the fact that there are several surveys on IoV security [ 12 , 65 , 67 , 69 , 71 , 72 , 73 , 74 , 75 ], few surveys have covered ML-based solutions in security or in trust schemes in this environment. For example, the surveys in [ 50 , 70 , 76 ] addressed the use of ML-based trust evaluation in IoV focusing on the trust scheme.…”

Section: Related Surveysmentioning

confidence: 99%

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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: Related Surveysmentioning

confidence: 99%

Section: Related Surveysmentioning

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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“…Consequently, a piece of secret information is captured and misused for different malicious purposes, which can cause serious interruptions [5]. Moreover, the constant exchange of information between road entities makes IoV an attractive target for eavesdroppers [6]. Such vulnerability raises significant concerns, as it can potentially lead to malicious activities that can endanger the safety, security, and privacy of the vehicle system.…”

Section: Introductionmentioning

confidence: 99%

A Distributed Lightweight PUF-Based Mutual Authentication Protocol for IoV

Alkanhal,

Alali,

Younis

2023

IoT

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In recent times, the advent of innovative technological paradigms like the Internet of Things has paved the way for numerous applications that enhance the quality of human life. A remarkable application of IoT that has emerged is the Internet of Vehicles (IoV), motivated by an unparalleled surge of connected vehicles on the roads. IoV has become an area of significant interest due to its potential in enhancing traffic safety as well as providing accurate routing information. The primary objective of IoV is to maintain strict latency standards while ensuring confidentiality and security. Given the high mobility and limited bandwidth, vehicles need to have rapid and frequent authentication. Securing Vehicle-to-Roadside unit (V2R) and Vehicle-to-Vehicle (V2V) communications in IoV is essential for preventing critical information leakage to an adversary or unauthenticated users. To address these challenges, this paper proposes a novel mutual authentication protocol which incorporates hardware-based security primitives, namely physically unclonable functions (PUFs) with Multi-Input Multi-Output (MIMO) physical layer communications. The protocol allows a V2V and V2R to mutually authenticate each other without the involvement of a trusted third-party (server). The protocol design effectively mitigates modeling attacks and impersonation attempts, where the accuracy of predicting the value of each PUF response bit does not exceed 54%, which is equivalent to a random guess.

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On the secure performance of Internet of Vehicles in Nakagami-m fading scenarios

Liu

Yan

Shuai

et al. 2023

Physical Communication

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Security Threats & Attacks in IoV Environment: Open Research Issues and Challenges

Cited by 4 publications

References 0 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

A Distributed Lightweight PUF-Based Mutual Authentication Protocol for IoV

On the secure performance of Internet of Vehicles in Nakagami-m fading scenarios

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