Secure Authentication Mechanism for Cluster based Vehicular Adhoc Network (VANET): A Survey
- URL: http://arxiv.org/abs/2312.12925v1
- Date: Wed, 20 Dec 2023 10:58:43 GMT
- Title: Secure Authentication Mechanism for Cluster based Vehicular Adhoc Network (VANET): A Survey
- Authors: Rabia Nasir, Humaira Ashraf, NZ Jhanjhi,
- Abstract summary: Vehicular Ad Hoc Networks (VANETs) play a crucial role in Intelligent Transportation Systems (ITS) by facilitating communication between vehicles and infrastructure.
This survey paper presents a comprehensive analysis of existing authentication mechanisms proposed for cluster-based VANETs.
The integration of secure key management techniques is discussed to enhance the overall authentication process.
- Score: 1.0070449177493677
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Vehicular Ad Hoc Networks (VANETs) play a crucial role in Intelligent Transportation Systems (ITS) by facilitating communication between vehicles and infrastructure. This communication aims to enhance road safety, improve traffic efficiency, and enhance passenger comfort. The secure and reliable exchange of information is paramount to ensure the integrity and confidentiality of data, while the authentication of vehicles and messages is essential to prevent unauthorized access and malicious activities. This survey paper presents a comprehensive analysis of existing authentication mechanisms proposed for cluster-based VANETs. The strengths, weaknesses, and suitability of these mechanisms for various scenarios are carefully examined. Additionally, the integration of secure key management techniques is discussed to enhance the overall authentication process. Cluster-based VANETs are formed by dividing the network into smaller groups or clusters, with designated cluster heads comprising one or more vehicles. Furthermore, this paper identifies gaps in the existing literature through an exploration of previous surveys. Several schemes based on different methods are critically evaluated, considering factors such as throughput, detection rate, security, packet delivery ratio, and end-to-end delay. To provide optimal solutions for authentication in cluster-based VANETs, this paper highlights AI- and ML-based routing-based schemes. These approaches leverage artificial intelligence and machine learning techniques to enhance authentication within the cluster-based VANET network. Finally, this paper explores the open research challenges that exist in the realm of authentication for cluster-based Vehicular Adhoc Networks, shedding light on areas that require further investigation and development.
Related papers
- AI-Driven Intrusion Detection Systems (IDS) on the ROAD Dataset: A Comparative Analysis for Automotive Controller Area Network (CAN) [4.081467217340597]
The Controller Area Network (CAN) bus is a central system for managing in-vehicle communication between the electronic control units (ECUs)
CAN protocol poses security challenges due to inherent vulnerabilities, lacking encryption and authentication, which, combined with an expanding attack surface, necessitates robust security measures.
This paper considers the latest ROAD dataset, containing stealthy and sophisticated injections.
arXiv Detail & Related papers (2024-08-30T12:26:23Z) - Securing the Open RAN Infrastructure: Exploring Vulnerabilities in Kubernetes Deployments [60.51751612363882]
We investigate the security implications of and software-based Open Radio Access Network (RAN) systems.
We highlight the presence of potential vulnerabilities and misconfigurations in the infrastructure supporting the Near Real-Time RAN Controller (RIC) cluster.
arXiv Detail & Related papers (2024-05-03T07:18:45Z) - Decentralized Multimedia Data Sharing in IoV: A Learning-based Equilibrium of Supply and Demand [57.82021900505197]
Internet of Vehicles (IoV) has great potential to transform transportation systems by enhancing road safety, reducing traffic congestion, and improving user experience through onboard infotainment applications.
Decentralized data sharing can improve security, privacy, reliability, and facilitate infotainment data sharing in IoVs.
We propose a decentralized data-sharing incentive mechanism based on multi-intelligent reinforcement learning to learn the supply-demand balance in markets.
arXiv Detail & Related papers (2024-03-29T14:58:28Z) - Deep Learning Approaches for Network Traffic Classification in the
Internet of Things (IoT): A Survey [0.0]
The Internet of Things (IoT) has witnessed unprecedented growth, resulting in a massive influx of diverse network traffic from interconnected devices.
Effectively classifying this network traffic is crucial for optimizing resource allocation, enhancing security measures, and ensuring efficient network management in IoT systems.
Deep learning has emerged as a powerful technique for network traffic classification due to its ability to automatically learn complex patterns and representations from raw data.
arXiv Detail & Related papers (2024-02-01T14:33:24Z) - Effective Intrusion Detection in Heterogeneous Internet-of-Things Networks via Ensemble Knowledge Distillation-based Federated Learning [52.6706505729803]
We introduce Federated Learning (FL) to collaboratively train a decentralized shared model of Intrusion Detection Systems (IDS)
FLEKD enables a more flexible aggregation method than conventional model fusion techniques.
Experiment results show that the proposed approach outperforms local training and traditional FL in terms of both speed and performance.
arXiv Detail & Related papers (2024-01-22T14:16:37Z) - A Survey and Comparative Analysis of Security Properties of CAN Authentication Protocols [92.81385447582882]
The Controller Area Network (CAN) bus leaves in-vehicle communications inherently non-secure.
This paper reviews and compares the 15 most prominent authentication protocols for the CAN bus.
We evaluate protocols based on essential operational criteria that contribute to ease of implementation.
arXiv Detail & Related papers (2024-01-19T14:52:04Z) - A Novel Federated Learning-Based IDS for Enhancing UAVs Privacy and Security [1.2999518604217852]
Unmanned aerial vehicles (UAVs) operating within Flying Ad-hoc Networks (FANETs) encounter security challenges due to the dynamic and distributed nature of these networks.
Previous studies predominantly focused on centralized intrusion detection, assuming a central entity responsible for storing and analyzing data from all devices.
This paper introduces the Federated Learning-based Intrusion Detection System (FL-IDS), addressing challenges encountered by centralized systems in FANETs.
arXiv Detail & Related papers (2023-12-07T08:50:25Z) - Federated Deep Learning Meets Autonomous Vehicle Perception: Design and
Verification [168.67190934250868]
Federated learning empowered connected autonomous vehicle (FLCAV) has been proposed.
FLCAV preserves privacy while reducing communication and annotation costs.
It is challenging to determine the network resources and road sensor poses for multi-stage training.
arXiv Detail & Related papers (2022-06-03T23:55:45Z) - Federated Learning for Intrusion Detection System: Concepts, Challenges
and Future Directions [0.20236506875465865]
Intrusion detection systems play a significant role in ensuring security and privacy of smart devices.
The present paper aims to present an extensive and exhaustive review on the use of FL in intrusion detection system.
arXiv Detail & Related papers (2021-06-16T13:13:04Z) - Survey of Network Intrusion Detection Methods from the Perspective of
the Knowledge Discovery in Databases Process [63.75363908696257]
We review the methods that have been applied to network data with the purpose of developing an intrusion detector.
We discuss the techniques used for the capture, preparation and transformation of the data, as well as, the data mining and evaluation methods.
As a result of this literature review, we investigate some open issues which will need to be considered for further research in the area of network security.
arXiv Detail & Related papers (2020-01-27T11:21:05Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.