PPSA: Privacy Preserved and Secured Architecture for Internet of Vehicles

Jinila, Y. Bevish; Sheeba, G. Merlin; Shyry, S. Prayla

doi:10.1007/s11277-021-08179-8

Cited by 4 publications

(4 citation statements)

References 23 publications

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“…IoV networks require software applications to monitor vehicle movements and provide security against malicious attacks. These systems function through interactions with various components, including vehicle communication with roads, roadside units, and sensors [5]. IoV brings together two cutting-edge dreams-the network and intelligent vehicles-while centering around the objects (e.g., humans, vehicles, systems) to create a perceptive system that relies on information technology and communication features to assist authorities in huge urban territories and entire countries [3].…”

Section: Background 21 An Overview Of Internet Of Vehiclesmentioning

confidence: 99%

“…Roadside Units (RSUs): These are fixed infrastructural components placed alongside roads. RSUs facilitate communication between vehicles and the broader network infrastructure, acting as access points for data transmission and reception [5].…”

mentioning

confidence: 99%

“…Traffic Management Center (TMC): The TMC acts as the control hub for traffic management, receiving data from various sources and making decisions to optimize traffic flow, reduce congestion, and enhance road safety[5].•Cloud and Edge Computing Resources: Cloud computing provides vast storage and processing capabilities, essential for handling the large volumes of data generated in IoV. Edge computing, on the other hand, offers localized processing at the network edge, enabling real-time data processing and decision-making.…”

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confidence: 99%

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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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Section: Background 21 An Overview Of Internet Of Vehiclesmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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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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“…Privacy is a leading role in the IoV network, in which there is no trust in any service if privacy is violated. erefore, privacy is a pivotal point in protecting IoV from many threats [54]. As part of IoV technology, vehicles share collected information about locations, traffic conditions, speed, and environmental information with other vehicles.…”

Section: Privacymentioning

confidence: 99%

Architecture, Protocols, and Security in IoV: Taxonomy, Analysis, Challenges, and Solutions

Karim

Habbal

Chaudhry

et al. 2022

Security and Communication Networks

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Internet of Vehicles (IoV) is a multinode network that exchanges information in an open, wireless environment. Various communication activities exist between IoV entities to share important information such as (ID, location, speed, messages, and traffic information), necessary for network operation. As part of intelligent transportation, IoV is considered a hot subject for researchers, because it is still facing many unresolved challenges, especially those concerning security and privacy. The variation of security-privacy threats that can menace the safety, privacy, and lives of vehicle occupants makes security the leading point of interest. The development of communication protocols for autonomous vehicles opens to us new issues to study and enhance the performance of IoV networks in terms of security and privacy. Several works have been reported, proposing many solutions for practical security challenges including a considerable number of survey-review papers published in respectable channels. The main motive of this review paper is to present the latest developments related to IoV security, as well as to address existing limitations. The high frequency of publication on IoV architecture, security, and new solutions leads us to write a compact, comprehensive, and up-to-date review. Inclusion criteria for selected papers include recent publications, number of citations, and impact of the research. In the present survey paper, the IoV architecture model is defined with all related communication types, and security and privacy issues are analyzed and presented with recently proposed solutions in a clear method. Clear classifications of threats, attacks, protocols, and solutions are presented. Moreover, the use of blockchain-based IoV to improve system security is discussed highlighting the most important trends and taxonomies. The paper was written to be a candidate as the first to read on the topic of the IoV security challenge, presenting problems and solutions in a clear, smooth, complete, and integrated manner.

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Group Based Dual Mode Key Management Scheme for Secure Communication in Vehicular Ad Hoc Networks

Ramamoorthy¹,

Thangavelu²

2021

Wireless Pers Commun

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PPSA: Privacy Preserved and Secured Architecture for Internet of Vehicles

Cited by 4 publications

References 23 publications

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

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

Architecture, Protocols, and Security in IoV: Taxonomy, Analysis, Challenges, and Solutions

Group Based Dual Mode Key Management Scheme for Secure Communication in Vehicular Ad Hoc Networks

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