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Security issues in Internet of Vehicles (IoV): A comprehensive survey

INTERNET OF THINGS (2023)

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A Survey of Security and Privacy Issues in V2X Communication Systems

Takahito Yoshizawa et al.

Summary: Vehicle-to-Everything (V2X) communication is gaining attention as pilot projects explore its capabilities. However, our survey reveals security and privacy issues and inconsistencies in relevant standards. We analyze the root causes and categorize the identified issues, providing recommendations for improving security and safety in vehicular communication.

ACM COMPUTING SURVEYS (2023)

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Article Computer Science, Information Systems

MTH-IDS: A Multitiered Hybrid Intrusion Detection System for Internet of Vehicles

Li Yang et al.

Summary: Modern vehicles, including connected vehicles and autonomous vehicles, are vulnerable to cyber-attacks due to their increasing functionality and connectivity. To secure vehicular networks, researchers propose a hybrid intrusion detection system that can detect both known and unknown attacks.

IEEE INTERNET OF THINGS JOURNAL (2022)

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Article Computer Science, Hardware & Architecture

CAN Bus Intrusion Detection Based on Auxiliary Classifier GAN and Out-of-distribution Detection

Qingling Zhao et al.

Summary: The Controller Area Network (CAN) is a widely used bus protocol in vehicle Electrical/Electronic (E/E) systems, but it is vulnerable to attacks. This paper presents a method based on ACGAN to detect intrusions on the CAN bus, assign labels to known attacks, and detect unknown attacks. The most effective method is a cascaded two-stage classification architecture, suitable for deployment in resource-constrained in-vehicle environments.

ACM TRANSACTIONS ON EMBEDDED COMPUTING SYSTEMS (2022)

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Histogram-Based Intrusion Detection and Filtering Framework for Secure and Safe In-Vehicle Networks

Abdelouahid Derhab et al.

Summary: In this paper, a Histogram-based Intrusion Detection and Filtering framework called H-IDFS is proposed, which assembles CAN packets into windows and computes histograms for classification. A novel one-class SVM named OCSVM-attack is introduced for filtering out normal CAN packets from malicious windows. Experimental results demonstrate the superiority of H-IDFS in window classification and normal packet filtering.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2022)

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Article Engineering, Civil

Federated Intrusion Detection in Blockchain-Based Smart Transportation Systems

Mohamed Abdel-Basset et al.

Summary: With the integration of IoT in transportation, Internet of Vehicles has become crucial for designing Smart Transportation Systems. This study introduces a federated deep learning-based intrusion detection framework to efficiently detect attacks. The framework utilizes context-aware transformer network and blockchain-managed federated training for secure and reliable training without centralized authority, demonstrating its effectiveness against state-of-the-art approaches in securing intelligent transportation systems against cyber-attacks.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2022)

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3D Object Detection for Autonomous Driving: A Survey

Rui Qian et al.

Summary: Autonomous driving is considered a promising solution to protecting humans from serious accidents. 3D object detection plays a crucial role in the perception system, specifically in path planning, motion prediction, and collision avoidance. However, the field of 3D object detection for autonomous driving is still in its early stages, facing challenges such as visual appearance recovery without depth information, representation learning from partially occluded point clouds, and semantic alignments across different modalities. This comprehensive survey aims to address this gap by examining sensors, datasets, performance metrics, and recent state-of-the-art detection methods, along with their advantages and disadvantages. The article also provides quantitative comparisons and case studies, including runtime analysis, error analysis, and robustness analysis.

PATTERN RECOGNITION (2022)

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Review Computer Science, Information Systems

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

Sulaiman M. Karim et al.

Summary: Internet of Vehicles (IoV) is a multinode network that exchanges information in an open, wireless environment. Researchers have focused on the security challenges of IoV due to the unresolved issues and potential threats to safety and privacy. This review presents the latest developments and proposed solutions for IoV security, providing a clear and comprehensive analysis of the architecture, communication types, threats, attacks, protocols, and the use of blockchain for system security.

SECURITY AND COMMUNICATION NETWORKS (2022)

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Transfer learning based intrusion detection scheme for Internet of vehicles

Xinghua Li et al.

Summary: As a new type of network, attacks in the Internet of Vehicles (IoV) are constantly evolving. This paper proposes two model update schemes based on whether the IoV cloud can provide timely labeled data for new attacks. The experimental results show that the proposed schemes have significantly improved detection accuracy compared to existing methods.

INFORMATION SCIENCES (2021)

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Article Computer Science, Information Systems

Intrusion detection on internet of vehicles via combining log-ratio oversampling, outlier detection and metric learning

Fusheng Jin et al.

Summary: This paper presents an intrusion detection method combining oversampling, outlier detection, and metric learning to improve the accuracy and rapid detection of malicious attacks in the IoV environment. Experimental results demonstrate the effectiveness of the proposed method in achieving high accuracy on different datasets.

INFORMATION SCIENCES (2021)

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Ad Hoc Vehicular Fog Enabling Cooperative Low-Latency Intrusion Detection

Azzam Mourad et al.

Summary: The article discusses the challenges of intrusion detection in Internet of Vehicles and vehicular networks, and proposes a vehicular-edge computing (VEC) fog-enabled scheme to offload intrusion detection tasks with minimal latency. The scheme aims to maximize offloading survivability while minimizing computation execution time and energy consumption.

IEEE INTERNET OF THINGS JOURNAL (2021)

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Article Computer Science, Theory & Methods

Cyberattacks and Countermeasures for In-Vehicle Networks

Emad Aliwa et al.

Summary: The increasing connectivity within vehicles has raised concerns about safety and security, especially regarding automotive serial protocols like CAN Bus. Current security mechanisms focus on encryption, authentication, and IDS, but face limitations due to constraints like low bandwidth and real-time sensitivity. Future research needs to address these limitations and challenges in order to improve the security of connected vehicles.

ACM COMPUTING SURVEYS (2021)

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Article Computer Science, Information Systems

AS-IDS: Anomaly and Signature Based IDS for the Internet of Things

Yazan Otoum et al.

Summary: The proposed AS-IDS model combines anomaly-based and signature-based approaches to detect known and unknown attacks in IoT networks. The model consists of three phases: traffic filtering, preprocessing, and hybrid IDS, with various methods used to process incoming traffic and classify attacks.

JOURNAL OF NETWORK AND SYSTEMS MANAGEMENT (2021)

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A new distributed architecture for evaluating AI-based security systems at the edge: Network TON_IoT datasets

Nour Moustafa

Summary: This paper introduces a novel testbed architecture of IoT network utilizing NSX vCloud NFV platform to execute SDN, NFV, and SO, offering dynamic testbed networks. By deploying real-world scenarios, the 'TON_IoT' dataset is generated and validated using four machine learning-based intrusion detection algorithms, showing high detection accuracy. The diverse patterns in TON_IoT dataset can be used to better validate new AI-based security solutions.

SUSTAINABLE CITIES AND SOCIETY (2021)

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Collaborative Intrusion Detection for VANETs: A Deep Learning-Based Distributed SDN Approach

Jiangang Shu et al.

Summary: Vehicular Ad hoc Network (VANET) is vulnerable to intrusion attacks, and a collaborative intrusion detection system (CIDS) utilizing deep learning and generative adversarial networks has been designed to address this issue. The CIDS enables global monitoring of abnormal network behaviors in the entire VANET.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2021)

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Blockchain and Federated Learning for Collaborative Intrusion Detection in Vehicular Edge Computing

Hong Liu et al.

Summary: This research proposes a cooperative intrusion detection mechanism that offloads the training model to distributed edge devices, reducing resource utilization of the central server while ensuring security and privacy. It uses blockchain for storing and sharing training models, achieving cooperative privacy-preservation for vehicles while reducing communication overhead and computation cost.

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY (2021)

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Artificial Intelligence (AI)-Empowered Intrusion Detection Architecture for the Internet of Vehicles

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IEEE WIRELESS COMMUNICATIONS (2021)

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A Deep Blockchain Framework-Enabled Collaborative Intrusion Detection for Protecting IoT and Cloud Networks

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Summary: Significant research has been done on combining blockchain and intrusion detection for enhanced data privacy and detection of cyberattacks. Learning-based ensemble models can identify complex malicious events while ensuring data privacy, providing additional security during VM migration and IoT network protection. The deep blockchain framework proposed in this study outperforms peer models and has potential as a decision support system for secure data migration.

IEEE INTERNET OF THINGS JOURNAL (2021)

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Intrusion Detection System Using Machine Learning for Vehicular Ad Hoc Networks Based on ToN-IoT Dataset

Abdallah R. Gad et al.

Summary: VANETs are a subsystem of the proposed intelligent transportation system, allowing vehicles to communicate wirelessly but vulnerable to attacks. Researchers are interested in enhancing security with machine learning methods. Experimental results show that XGBoost outperforms other ML methods.

IEEE ACCESS (2021)

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Empirical Evaluation of Attacks Against IEEE 802.11 Enterprise Networks: The AWID3 Dataset

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Summary: This work aims to enhance the AWID corpus by studying attacks in the IEEE 802.1X and 802.11w protocols, offering insights for designing and evaluating intrusion detection systems.

IEEE ACCESS (2021)

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IEEE OPEN JOURNAL OF VEHICULAR TECHNOLOGY (2020)

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