Security In Internet Of Vehicles Research Articles

A comprehensive intrusion detection method for the internet of vehicles based on federated learning architecture

Cybersecurity breaches within the Internet of Vehicles (IoV) have been increasingly reported annually with the proliferation of intelligent connected vehicles. Two primary obstacles are faced by current intrusion detection systems: substantial computational demands and stringent data privacy regulations, complicating both efficient deployment and the safeguarding of data privacy. Consequently, there is a pressing need for intrusion detection solutions that are both efficient and considerate of privacy concerns. This paper introduces FED-IoV, an innovative intrusion detection method tailored for the IoV, leveraging a federated learning architecture. FED-IoV aims to collaboratively perform detection tasks across distributed edge devices, thereby minimizing data privacy risks. Vehicular communication traffic data is transformed into images, and a bespoke, efficient model, MobileNet-Tiny, is employed for feature extraction, rendering FED-IoV capable of achieving high detection accuracy whilst being viable for deployment on devices with limited resources. Through evaluation against the authoritative datasets CAN-Intrusion and CICIDS2017, exceptional accuracy rates of 98.51 % and 97.74 %, respectively, were demonstrated by FED-IoV within a federated learning context, and excellent detection capabilities on imbalanced datasets were also shown. Moreover, a prediction latency of under 10 milliseconds per sample was maintained on devices with limited computational power, such as the Raspberry Pi 4 8GB, showcasing significantly better accuracy and real-time performance relative to existing approaches. The successful deployment of FED-IoV ushers in a novel, privacy-preserving, and efficient intrusion detection solution for IoV security.

A Practical Approach to Intrusion Detection in IoV Networks Using LCCDE Ensemble Framework

Abstract: With the rapid development of the Internet of Things (IoT) and the Internet of Vehicles (IoV) technologies, modern vehicles are increasingly adopting network-controlled functionalities, exposing them to a variety of cyber threats. To address these security challenges, this paper implements and evaluates a novel ensemble Intrusion Detection System (IDS) framework, named Leader Class and Confidence Decision Ensemble (LCCDE). The LCCDE framework integrates three advanced Machine Learning (ML) algorithms—XGBoost, LightGBM, and CatBoost—to detect various types of cyber-attacks on both intra-vehicle and external vehicular networks.This study demonstrates the effectiveness of the LCCDE framework using two public IoV security datasets: the Car-Hacking dataset and the CICIDS2017 dataset. By identifying the best-performing ML model for each class of attacks and leveraging prediction confidence values, LCCDE achieves high detection accuracy and robustness against diverse attack types. The experimental results show that the proposed framework outperforms traditional IDS approaches in terms of detection accuracy, computational efficiency, and adaptability to different types of cyber-attacks. This paper provides practical insights into the implementation and deployment of IDS in IoV systems, highlighting the potential of ensemble learning methods to enhance vehicular network security.

Artificial intelligence powered internet of vehicles: securing connected vehicles in 6G

<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p>The rapid advancements in automotive technology and the emergence of next-generation networks such as 5G and 6G are laying the foundation for the internet of vehicles (IoV), a revolutionary concept to transform transportation systems. The convergence of artificial intelligence (AI) and connected vehicles IoV is driving a paradigm shift in the transportation sector, especially in the dynamic framework of 5G and future 6G networks. This survey paper provides a thorough survey of the evolving AI-based IoV security landscape. We explore key areas of 5G/6G networks, focusing on the complex interplay of machine learning (ML) and deep learning (DL) in enhancing vehicle-to-everything (V2X) security and connected vehicles. Addressing the unique challenges of 6G, this paper outlines future directions for improving security and highlights open research issues. This comprehensive survey, which aims to provide information and guidance to both researchers and practitioners, contributes to a detailed understanding of the security issues associated with connected vehicles in the emerging 6G era.</p></div></div></div>

Advanced machine learning approach for DoS attack resilience in internet of vehicles security

Recent years have witnessed security as a great concern in vehicular networks (VANET). Particularly, Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks can jeopardize the network by broadcasting a storm of packets. Correspondingly, the network resources are jammed with malicious traffic. In this connection, the existing research presented various techniques to cope with DoS and DDoS attacks. Different from those traditional approaches, this study proposes an Intelligent Intrusion Detection System (IDS) by leveraging Machine Learning (ML). The proposed IDS utilizes a publicly available dataset on the application layer for mitigating DDoS attacks. The designed ML-based IDS relies on combining both the Random Projection (RP) and Randomized Matrix Factorization (RMF) methods to achieve the best results for enhancing the detection capabilities of the IDS. This amalgamation enhances the system's detection capabilities by extracting and analyzing meaningful features from network traffic data. Experimental validation of our approach involves a comprehensive evaluation of various ML models, including Extra Tree Classifier (ETC), Logistic Regression (LR), and Random Forest (RF). Remarkably, the combined accuracy of these models yields an average system accuracy of 0.98, surpassing existing methods. Unlike conventional approaches, our proposed IDS excels in efficiency and exhibits notable performance in detecting DoS and DDoS attacks in VANET. This proficiency ensures the integrity and safety of vehicle communications. Thus, our research substantially contributes to the vehicular network security field. The presented findings establish a foundation for future advancements in securing connected vehicles.

Attribute and User Trust Score-Based Zero Trust Access Control Model in IoV

The Internet of Vehicles (IoV) is an innovative area of interest in modern mobility that is rapidly evolving while facing complex challenges. Traditional IoV networks are susceptible to intrusion threats, which can lead to data leakage and seizure of vehicle control by attackers, thereby endangering vehicle users’ privacy and personal safety. An Attribute and User Trust Score-based Zero Trust Access Control Model (AU-ZTAC) is proposed, combining the zero-trust and attribute-based access control models to meet network protection requirements while achieving fine-grained dynamic access control and incorporating trust evaluation in the access control process to better reflect users’ intent. Experimental results demonstrate the effectiveness and feasibility of trust assessment through the proposed model. A comparison with the classical schemes illustrates that AU-ZTAC allows for more flexible and fine-grained access control in complex access control environments while improving IoV security.

IoV data sharing scheme based on the hybrid architecture of blockchain and cloud-edge computing

Achieving efficient and secure sharing of data in the Internet of Vehicles (IoV) is of great significance for the development of smart transportation. Although blockchain technology has great potential to promote data sharing and privacy protection in the context of IoV, the problem of securing data sharing should be payed more attentions. This paper proposes an IoV data sharing scheme based on the hybrid architecture of blockchain and cloud-edge computing. Firstly, to improve protocol’s efficiency, a dual-chain structure empowered by alliance chain is introduced as the model architecture. Secondly, for the space problem characterized by data storage and security, we adopt distributed storage with the help of edge devices. Finally, to both ensure the efficiency of consensus protocol and protect the privacy of vehicles and owners simultaneously, we improve DPoS consensus algorithm to realize the efficient operation of the IoV data sharing model, which is closer to the actual needs of IoV. The comparison with other data sharing models highlights the advantages of this model, in terms of data storage and sharing security. It can be seen that the improved DPoS has high consensus efficiency and security in IoV.

Blockchain and trust-based clustering scheme for the IoV

The advent of the Internet of Vehicles (IoV) leads to the proliferation of services proposed for the users. Its adoption aims mainly to make driving more secure and enhance the users’ quality of experience. However, the deployment of multiple technologies that interact with each other brings out big security challenges. Indeed, it enlarges the attack vectors and surface. In the IoV ecosystem, cybersecurity issues can be driven from the security of communication links, security of devices, identity and liability, access control as well as privacy of drivers and vehicles. Trust management is one of the key mechanisms that can enforce the security of an IoV network. In a previous work (Hbaieb et al., 2021), we proposed a blockchain based trust management framework to enhance IoV security. Our framework showed good performance results. However, it was based on a centralized trust based approach. Thus, its performance is decreasing when dealing with effective scalability. Moreover, we did not focus on the energy consumption of the proposed model. In this paper, we propose an extension to enhance the highlighted limitations. For that, we are based on a decentralized trust process. We, also, introduce a clustering mechanism to construct our framework. Moreover, to take into account the quality of service (QoS) of our framework while enforcing security, we define our clustering process based on different parameters such as the trust value, the safety distance, and the energy factor. The evaluation of our proposal shows relevant results in terms of detection performance, communication overhead and runtime properties. The simulation results indicate that the formation of trustworthy clusters using blockchain reinforces the IoV reliability.

BSDCE-IoV: Blockchain-Based Secure Data Collection and Exchange Scheme for IoV in 5G Environment

The Internet of Vehicles (IoV) is a network that connects vehicles and their environment: in-built devices, pedestrians, and infrastructure through the Internet using heterogeneous access technologies. During communication between vehicles, roadside units, and control rooms, data confidentiality and privacy are critical issues that require effective measures. Several works have been proposed for securing IoV environments based on vehicles-to-infrastructure authentication; However, some schemes have security vulnerabilities, while others have shown efficiency issues. Due to its decentralization, stability, and transaction tracking capabilities, Blockchain as an emerging technology presents a potential solution for IoV security. This article provides an in-depth examination of the benefits of blockchain for a 5G-based IoV environment. In particular, we propose and evaluate a novel blockchain-based secure data exchange (BSDCE-IoV) scheme based on Elliptic Curve Cryptography algorithm. Our solution is designed to eliminate several potential attacks that pose a threat to the IoV environment. Deep examination using the Real-or-Random oracle model and Scyther tool, in addition to the informal security analysis, validates the scheme regarding security and privacy. The Multi-precision Integer and Rational Arithmetic Cryptographic Library (MIRACL) assesses the computational and communication overhead. Computational and communicative overheads were also evaluated using the Multi-precision Integer and Rational Arithmetic Cryptographic Library (MIRACL). BSDCE-IoV shows higher performance in terms of security, functionality, and time delay than a number of recent selective work in IoV security.

AI-based Intrusion Detection for Intelligence Internet of Vehicles

With the development of intelligent technologies, Internet of Things (IoT) opens up a new era in the field of automotive networks, namely, Internet of Vehicles (IoV). The main goal of IoV is to provide a secure and reliable network to vehicles so that users can enjoy various services. However, vulnerabilities and incomplete protection mechanisms have led to a proliferation of security threats against IoV networks. Intrusion detection technology is an effective protection solution for IoV security, especially when artificial intelligence (AI) technology has been introduced into intrusion detection study. This article first briefly introduces the concept and features of IoV, and then reviews the related research on AI-based IoV intrusion detection systems. Finally, we discuss the open challenges and future research directions.

Introduction to the Special Section on Cyber Security in Internet of Vehicles

introduction Share on Introduction to the Special Section on Cyber Security in Internet of Vehicles Authors: Ching-Hsien Hsu Department of Computer Science and Information Engineering, Asia University, Taiwan; Department of Medical Research, China Medical University Hospital, China Medical University, Wufeng, Taichung, Taiwan Department of Computer Science and Information Engineering, Asia University, Taiwan; Department of Medical Research, China Medical University Hospital, China Medical University, Wufeng, Taichung, Taiwan 0000-0002-2440-2771Search about this author , Amir H. Alavi Department of Civil and Environmental Engineering, University of Pittsburgh, Pittsburgh, PA, USA Department of Civil and Environmental Engineering, University of Pittsburgh, Pittsburgh, PA, USA 0000-0002-7593-8509Search about this author , Mianxiong Dong Department of Sciences and Informatics, Muroran Institute of Technology, Muroran, Hokkaido, Japan Department of Sciences and Informatics, Muroran Institute of Technology, Muroran, Hokkaido, Japan 0000-0002-2788-3451Search about this author Authors Info & Claims ACM Transactions on Internet TechnologyVolume 22Issue 4November 2022 Article No.: 81pp 1–6https://doi.org/10.1145/3584746Published:15 March 2023Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Federated AI-Enabled In-Vehicle Network Intrusion Detection for Internet of Vehicles

The integration of artificial intelligence (AI) technology into the Internet of Vehicles (IoV) has provided smart services for intelligent connected vehicles (ICVs). However, due to gradually upgrading to ICVs, an increasing number of external communications interfaces exposes the in-vehicle networks (IVNs) to malicious network intrusion. The malicious intruders can take over the compromised ICVs and mediately intrude into the ICVs connected through IoV. Therefore, it is urgent to develop IVN intrusion detection methods for IoV security protection. In this paper, a ConvLSTM-based IVN intrusion detection method is developed by leveraging the periodicity of the network message ID. For training the ConvLSTM model, a federated learning (FL) framework with client selection is proposed. The fundamental FL framework works in the client-server mode. ICVs are the local clients, and mobile edge computing (MEC) servers connected to base stations (BSs) function as the parameter servers. Based on the framework, a proximal policy optimization (PPO)-based federated client selection (FCS) scheme is further developed to optimize the model accuracy and system overhead of federated ConvLSTM model training. Simulations are conducted by the exploitation of real-world IoV scenario settings and IVN datasets. The results indicate that by exploiting the ConvLSTM, the model size and convergence time are dramatically reduced, and the 95%-beyond detection accuracy is maintained. The results also unveil that the PPO-based FCS scheme outperforms the benchmarks on the convergence rate, model accuracy, and system overhead.

Data‐centric approaches in the Internet of Vehicles: A systematic review on techniques, open issues, and future directions

SummaryThe Internet of Vehicles (IoV) is an emerging network of connected vehicles as a branch of dynamic objects in the Internet of Things (IoT) ecosystem. With the rapid development of IoV, real‐time data‐centric applications would be a significant concern in academia and industry to promote efficiency and realize modern services in such high dynamic networks. In this paper, we aim to present a systematic literature review (SLR) for the IoV networks to investigate the different attitudes in the field of data‐centric approaches. This paper systematically categorizes the 48 recent articles on data‐driven techniques in the IoV field published from 2017 to March 2022. A complete technical taxonomy is presented for the data‐centric approaches in IoV according to the content of current studies. Collected methods are chosen with the SLR process, and they are investigated considering some technical classifications including IoV security, data traffic, vehicular social network, data propagation, energy, and multimedia categories. The achievements, drawbacks, and new findings of studies are carefully investigated for addressing the deficiencies, as well as emphasizing future research direction and open issues of data‐driven approaches in IoV.

Blockchain-Empowered AI for 6G-Enabled Internet of Vehicles

The 6G communication technologies are expected to provide fast data rates and incessant connectivity to heterogeneous networks, such as the Internet of Vehicles (IoV). However, the resulting unprecedented surge in data traffic, massive increase in the number of nodes with high mobility, and low-latency requirements give rise to serious security, privacy, and trust challenges. The blockchain could potentially ensure trust and security in IoV due to its features, including consensus for credibility and immutability for tamper proofing. In parallel, federated learning (FL) is a privacy-preserving artificial-intelligence paradigm that does not require to share data for model training in machine learning. It can reduce data traffic and resolve privacy challenges of intelligent IoV networks. The blockchain can also complement FL by ensuring the decentralization and securing distribution of incentives. This article reviews the trends and challenges of the blockchain and FL in 6G IoV networks. Then, the impact of their combination, challenges in implementation, and future research directions are highlighted. We also evaluate our proposal of blockchain-based FL to protect IoV security and privacy that utilizes smart contract and secure transactions of incentives via the blockchain to protect FL. Compared with other solutions, the failure rate of the proposed solution was at least 5% lower with 30% malicious nodes in the network.

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

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.

Improvised Protocol for Enhancement of Security in Internet of Vehicles

Background: Vehicular Ad-Hoc Network (VANET) is integral to lessen road mishappenings and to effortlessly control huge traffic on highways. Numerous protocols are researched and implemented for creating a secure medium between vehicular nodes. Objective: One of the visible problems is the loss of data between the nodes, which leads to delay, collision, and accidents in the VANET. This paper investigates traffic security and safety problems faced in VANET and provides a solution for them. Methods: For analyses of traffic safety issues, 3 types of Medium Access Control (MAC) protocols were compared, namely Conventional, Contention-Based and Contention-Free MAC Protocols. Plentiful of performance metrics have been studied under the transportation security issues, including signal received with error, throughput, and MAC Overhead and Packet loss. Results and Conclusion: By comparing the MAC Protocols, it can be concluded that Contention-Free multi-channel SD-TDMA is better with a security mechanism for continuous and safe communication between the vehicular nodes. It can be utilized in moderate to heavy traffic scenarios to have faster and safe communication.

CTMF: Context-Aware Trust Management Framework for Internet of Vehicles

Secure communication is the top concern of the Internet of Vehicles (IoV). The trust between nodes can have a considerable impact on ensuring IoV security. Therefore, the trustworthiness of a received message must be evaluated before acting upon it. A malicious node can broadcast bogus events to obtain network control. False reports and malicious vehicles render the network unreliable during emergencies. In this study, a unique trust framework is presented that considers most of the aspects of trust in IoV to accurately identify malicious nodes and events. Previous studies have proposed some trust models for VANETs, which have many deficiencies in serving IoV. In particular, they lack dynamism and practical implementations. All the existing models have two things in common, first they work on fixed parameters, and second, they use static scenarios. In contrast, the proposed framework is based on a context-awareness cognitive approach with artificial intelligence (AI) properties. The framework cognitively learns the environment from the received report and creates a context around an event. In addition to trust management (TM), the proposed framework offers a novel process for detecting and screening malicious nodes using anomaly outliers. The performance of the framework was examined using an experimental simulation. The proposed framework was compared with top benchmarks in the field. The results show inclining performance indicators. The proposed trust-management framework has the potential to serve as a component of IoV security.

Blockchain-Based Authentication in Internet of Vehicles: A Survey.

Internet of Vehicles (IoV) has emerged as an advancement over the traditional Vehicular Ad-hoc Networks (VANETs) towards achieving a more efficient intelligent transportation system that is capable of providing various intelligent services and supporting different applications for the drivers and passengers on roads. In order for the IoV and VANETs environments to be able to offer such beneficial road services, huge amounts of data are generated and exchanged among the different communicated entities in these vehicular networks wirelessly via open channels, which could attract the adversaries and threaten the network with several possible types of security attacks. In this survey, we target the authentication part of the security system while highlighting the efficiency of blockchains in the IoV and VANETs environments. First, a detailed background on IoV and blockchain is provided, followed by a wide range of security requirements, challenges, and possible attacks in vehicular networks. Then, a more focused review is provided on the recent blockchain-based authentication schemes in IoV and VANETs with a detailed comparative study in terms of techniques used, network models, evaluation tools, and attacks counteracted. Lastly, some future challenges for IoV security are discussed that are necessary to be addressed in the upcoming research.

Designing an Efficient and Secure Message Exchange Protocol for Internet of Vehicles

In the advancements in computation and communication technologies and increasing number of vehicles, the concept of Internet of Vehicles (IoV) has emerged as an integral part of daily life, and it can be used to acquire vehicle related information including road congestion, road description, vehicle location, and speed. Such information is very vital and can benefit in a variety of ways, including route selection. However, without proper security measures, the information transmission among entities of IoV can be exposed and used for wicked intentions. Recently, many authentication schemes were proposed, but most of those authentication schemes are prone to insecurities or suffer from heavy communication and computation costs. Therefore, a secure message authentication protocol is proposed in this study for information exchange among entities of IoV (SMEP-IoV). Based on secure symmetric lightweight hash functions and encryption operations, the proposed SMEP-IoV meets IoV security and performance requirements. For formal security analysis of the proposed SMEP-IoV, BAN logic is used. The performance comparisons show that the SMEP-IoV is lightweight and completes the authentication process in just 0.198 ms .

A blockchain-based Roadside Unit-assisted authentication and key agreement protocol for Internet of Vehicles

A fundamental layer of smart cities, the Internet of Vehicles (IoV) can significantly improve transportation efficiency, reduce energy consumption, and traffic accidents. However, because of the vehicle and the RoadSide Units (RSU) use wireless channels for communication, the risk of information being leaked or tampered is highly increased. Therefore, secure and reliable authentication and key agreement protocol is the masterpiece of IoV security. As most of the existing authentication protocols pertain to a centralized structure and single Trusted Authority (TA) network model, all vehicles involved can only perform mutual authentication with one TA through the intermediate node RSU, and thus, the efficiency of these centralized authentication protocols is easily affected by TA’s communication and computing resource bottlenecks. In this article, a blockchain-based authentication and key agreement protocol is designed for the multi-TA network model, moving the computing load of TA down to the RSU to improve the efficiency of authentication. In addition, blockchain technology is used for multiple TAs to manage the ledger that stores vehicle-related information, which results in vehicles that can easily achieve cross-TA authentication. Both formal and informal security analysis and simulation results from ProVerif show that the proposed protocol is secure. Comparisons with other existing work show that the proposed protocol has less computational overhead, higher authentication efficiency, and can resist various common attacks.

Authentication Protocols in Internet of Vehicles: Taxonomy, Analysis, and Challenges

Internet of Vehicles (IoV) is treated as an extension of Vehicle-to-Vehicle (V2V) communication network. IoV helps in enhancing driving aids with the help of vehicle Artificial Intelligence (AI) awareness of other vehicles and their actions. IoV is connected in an adhoc networking environment which utilizes each vehicle in the network as a node, called Vehicular Ad Hoc Network (VANET), where the vehicles may be also connected to the public Internet. It is specifically important for the autonomous vehicles because they can instantaneously communicate with other vehicles surrounding them. In addition, safely avoiding accident prone zones is crucial in order to continue secure and smart transportation. Since the communication among various entities involved in the IoV environment is via open channel, it gives an opportunity to a passive/active adversary to intercept, modify, delete or even insert fake information during communication. It is then a serious concern for the vehicles users to determine whether the received information is genuine. In this survey paper, various security aspects, threats and attacks, network and threat models related to the IoV environment are discussed. Next, a taxonomy of security protocols is given that is essential to provide IoV data security. In particular, focus on various authentication protocols is given that is needed for mutual authentication among the involved entities in the IoV environment for secure communication. A detailed comparative analysis among various state-of-art authentication protocols proposed in the related IoV environment is provided to show their effectiveness as well as security and functionality features. Moreover, some testbeds are described that were designed and implemented for the IoV environment. In addition, some future challenges for IoV security protocols are also highlighted that are necessary to address in the future.