Security In Vehicular Networks Research Articles

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.

Clustered jamming in U-V2X communications with 3D antenna beam-width fluctuations

Jamming is the disruption of wireless communication signals, rendering them unreadable or degraded, typically caused by jamming devices. Clustered jamming in vehicular communications is a highly sophisticated attack technique characterized by the coordinated deployment of multiple jammers with the strategic intent to deliberately disrupt and incapacitate communication links, thereby presenting a formidable challenge to the reliability and security of vehicular networks. It is critical to address the intricate challenges imposed by clustered jamming in the context of vehicle-to-everything (V2X) communications augmented by unmanned aerial vehicles (UAVs), given their critical role in facilitating essential vehicle-to-vehicle and vehicle-to-UAV communication links. In this paper, we examine UAV-V2X (U-V2X) communications within the context of clustered jamming and the variations in the 3D beam-width of antennas. Also, mitigation techniques for clustered jamming in V2X communications are addressed. We consider the distribution of UAVs is governed by the Poisson point process, the distribution of vehicular-nodes (VNs) is governed by the Poisson line process, and the distribution of jammers is governed by the Matern Cluster Process. The analytical equations for the association probability and the success probability (SP) of a typical VN with the UAV and near-by VN, along with the presence of jammers and the variations of antenna beam-width are provided. Furthermore, we investigate the SP and spectral efficiency performance of the U-V2X network under different network configurations of UAVs, VNs, roads, jamming clusters, and transmit power of jamming devices. Our findings reveal that clustered jammers influence U-V2X network’s performance, further compounded by UAV beam-width fluctuations. Moreover, in low-density areas, UAVs demonstrate superior reliability for connectivity compared to VNs, despite clustered jamming. Also, lower UAV antenna fluctuations enhance UAVs’ dependability in U-V2X networks amidst clustered jamming. Specifically, for a given setup with τ= -5 dB, U-V2X link’s SP with jamming drops to one-quarter due to 2-degree beam-width fluctuations. Consequently, it is imperative to design and implement anti-jamming strategies, particularly as the number of jamming clusters and variations of antenna beam-width increase.

Optimized deep learning-based intrusion detection framework for vehicular network

The Internet’s evolution resulted in a massive amount of data. As a result, the internet has become more sophisticated and vulnerable to massive attacks. The attack detection system is a key feature for system security in modern networks. The IDS might be signature-based or detect anomalous behavior. Researchers recently created several detection algorithms for identifying network intrusions in vehicular network security, but they failed to detect intrusions effectively. For this reason, the optimal Deep Learning approach, namely Political Fractional Dingo Optimizer (PFDOX)-based Deep belief network is introduced for attack detection in network security for vehicles. The Internet of Vehicle simulation is done initially, and then the input data is passed into the pre-processing phase, which removes noise present in the data. Then, the feature extraction module receives the pre-processed data. The Deep Maxout Network is trained using the Fractional Dingo optimizer (FDOX)is utilized to detect normal and abnormal behavior. Fractional calculus and Dingo optimizer (DOX) are combined to create the proposed FDOX. Finally, intruder/attack types are classified using the Deep Belief Network, which is tuned using the PFDOX. The PFDOX is created by the assimilation of the DOX, Fractional Calculus, and Political Optimizer (PO). The experimental result shows that the designed PFDOX_DBN for attack type classification offers a better result based on f-measure, precision, and recall with the values of 0.924, 0.916, and 0.932, for the CIC-IDS2017 dataset.

A survey of security, privacy and trust issues in vehicular computation offloading and their solutions using blockchain.

Continuous improvement in transportation systems and smart vehicles' appearance make new highly intensive applications. Complex applications need high-performance capabilities, real-time responses, and generate massive amounts of data to process and exchange. This presents the idea of vehicular edge computing (VEC), which is proposed to handle complex applications and satisfy smart vehicle processing requirements. VEC enables computation offloading to an edge server to reduce communication latency, execution cost and energy consumption greatly. However, offloading to another node opens up new vulnerabilities regarding security and privacy. Moreover, trust issues in such an untrustworthy environment need an effective trust management solution and incentive mechanisms to improve overall security. This will increase the computation offloading success rate and the vehicles' willingness to share their resources. Particularly given the high transportability and heterogeneity of vehicular networks, the conventional security and trust management methods are inadequate. Blockchain, the rapidly emerging trend technology, is a unique solution that can help overcome security and privacy issues and meet trust management and incentive mechanism goals. Blockchain's immutable distributed ledger, traceability, consensus validation system and smart contract features can improve vehicular network security. Although most research is focused on enhancing the performance of computation offloading algorithms, blockchain security solutions in computation offloading scenarios are not fully discussed. Thus, security and trust issues related to computation offloading in VEC environments need more consideration since supporting the new complex vehicular applications is essential. Therefore, this paper provides a review of recent surveys and studies, an overview of VEC, computation offloading and blockchain, in addition to discussing security, privacy and trust in vehicular networks and computation offloading while considering blockchain as a distributed security solution. We propose a new paradigm called blockchain edge of vehicle (BEoV) at the end, which enables several blockchain-based security services for vehicular computation offloading in particular.

Toward a Distributed Trust Management System for Misbehavior Detection in the Internet of Vehicles

Recent considerable state-of-the-art advancements within the automotive sector, coupled with an evolution of the promising paradigms of vehicle-to-everything communication and the Internet of Vehicles (IoV), have facilitated vehicles to generate and, accordingly, disseminate an enormous amount of safety-critical and non-safety infotainment data in a bid to guarantee a highly safe, convenient, and congestion-aware road transport. These dynamic networks require intelligent security measures to ensure that the malicious messages, along with the vehicles that disseminate them, are identified and subsequently eliminated in a timely manner so that they are not in a position to harm other vehicles. Failing to do so could jeopardize the entire network, leading to fatalities and injuries amongst road users. Several researchers, over the years, have envisaged conventional cryptographic-based solutions employing certificates and the public key infrastructure for enhancing the security of vehicular networks. Nevertheless, cryptographic-based solutions are not optimum for an IoV network primarily, since the cryptographic schemes could be susceptible to compromised trust authorities and insider attacks that are highly deceptive in nature and cannot be noticed immediately and are, therefore, capable of causing catastrophic damage. Accordingly, in this article, a distributed trust management system has been proposed that ascertains the trust of all the reputation segments within an IoV network. The envisaged system takes into consideration the salient characteristics of familiarity, i.e., assessed via a subjective logic approach, similarity, and timeliness to ascertain the weights of all the reputation segments. Furthermore, an intelligent trust threshold mechanism has been developed for the identification and eviction of the misbehaving vehicles. The experimental results suggest the advantages of our proposed IoV-based trust management system in terms of optimizing the misbehavior detection and its resilience to various sorts of attacks.

Blockchain‐inspired lightweight trust‐based system in vehicular networks

SummaryA decentralized application runs on the blockchain network without the intervention of a central authority. Transparency in transactions and security in vehicular networks are the issues for central systems. The proposed system uses blockchain‐based smart contracts, which eliminate the requirement for any third‐party verification. Additionally, with signature verification and reduced overhead, smart contracts also help in a fast and secure transaction. This study suggests a trust‐based system paradigm where certificate authority (CA) is employed for vehicle registration. We also propose a blockchain‐based system that provides efficient two‐way authentication and key agreement through encryption and digital signatures. The analysis of the proposed model reveals that it is an efficient way of establishing distributed trust management, which helps in preserving vehicle privacy. The proposed scheme is tested in Automated Validation of Internet Security‐sensitive Protocols (AVISPA), and security parameters verification in Network Simulator 2(NS2) also shows that the proposed scheme is more effective in comparison with existing schemes in terms of authentication cost, storage cost, and overhead.

Blockchain enabled data security in vehicular networks

Recently, researchers have applied blockchain technology in vehicular networks to take benefit of its security features, such as confidentiality, authenticity, immutability, integrity, and non-repudiation. The resource-intensive nature of the blockchain consensus algorithm makes it a challenge to integrate it with vehicular networks due to the time-sensitive message dissemination requirements. Moreover, most of the researchers have used the Proof-of-Work consensus algorithm, or its variant to add a block to a blockchain, which is a highly resource-intensive process with greater latency. In this paper, we propose a consensus algorithm for vehicular networks named as Vehicular network Based Consensus Algorithm (VBCA) to ensure data security across the network using blockchain that maintains a secured pool of confirmed messages exchanged in the network. The proposed scheme, based on a consortium blockchain, reduces average transaction latency, and increases the number of confirmed transactions in a decentralized manner, without compromising the integrity and security of data. The simulation results show improved performance in terms of confirmed transactions, transaction latency, number of blocks, and block creation time.

Intrusion detection game for ubiquitous security in vehicular networks: A signaling game based approach

Wireless communications between smart vehicles can make travel quicker and more secure in vehicular networks. However, hackers can perform malicious activities against the communication system, which may cause accidents. To overcome this issue and insure universal public safety on roads, motoring organization needs to reinforce the security of the embedded systems to shield them from different vindictive attacks. In this research work, we present an intrusion detection approach to recognize noxious nodes. When an accident occurs, it gets stored in the vehicles data. Be that as it may, because of the presence of malicious nodes, this data can be erased from the network. Concerning this matter, we have developed a numerical model based on both the coalition game and the signaling game to design an Intrusion Detection Game (IDG). On the one hand, this approach aims to model the interactions between malicious nodes and the Coalition Head which is equipped with an Intrusion Detection System (CH-IDS). On the other hand, it intends to seek the Bayesian Nash Equilibrium (BNE) for an efficient intrusion detection. The simulation results have shown the effectiveness of the proposed approach, thus, the CH-IDS agents are able to select their optimal strategies to detect malicious nodes.

Toward the Design of an Efficient and Secure System Based on the Software-Defined Network Paradigm for Vehicular Networks

The advent of Intelligent Transport Systems (ITS) has led to the appearance of vehicles increasingly connected to their environment on global road networks. Due to the strict requirements for low latency and secure interactions in a vehicular environment, the proposal of new architectures is a crucial topic for discussion. This paper aims to develop a vehicular network using several access technologies based on SDN (Software Defined Network) paradigm, to take advantage of the capacities of the various access networks and provide flexibility in their control and management. Confidentiality, integrity, and authentication are essential services to prevent an adversary from compromising the security of vehicular networks. Therefore, good security and privacy management system is necessary to ensure this protection. We represent then a hybrid SDN-VANET architecture that can address all of the challenges we mentioned earlier. We are in the process of implementing a dynamic approach to optimize the positioning of controllers according to changes in network topology due to fluctuations in road traffic. We will also detail the topology estimation service based on machine learning techniques to provide network control functions with potential insight into the future state of the network, unlocking proactive and intelligent network control. We also provide a scheme that prevents and informs about basic and compound attacks and reacts to the privacy and security conditions of the vehicular network, managing the requirements of security management systems. The simulation results showed the effectiveness of the proposed schemes in terms of message loss rates, packet delivery rates (PDR), Round Trip Time (RTT), and delays. With used our scheme, the performance of the network is improved when SDN triggers the change of the RSU entity. Such as, we notice that the average RTT is lowered by 68 ms and that the PDR remains around 94%. We also notice with the integration of the security and privacy scheme (SPS) that the performance of the network is improved, the average RTT is reduced by 51 ms and the PDR persists around 99%.

Artificial Intelligence techniques to mitigate cyber-attacks within vehicular networks: Survey

Rapid advancements in communication technology have made vehicular networks a reality with numerous applications. However, vehicular network security is still an open research problem. Artificial intelligence (AI) techniques have emerged to address these issues. AI and its variants are becoming more popular for detecting attacks and dealing with many types of security issues in vehicular networks. This paper presents a comprehensive survey of AI-based techniques for security issues in vehicular networks. We first give a background on vehicular networks and their vulnerabilities. In addition, assess AI fundamentals with their impact on vehicular security. Furthermore, we classify and compare the AI-based solutions related to security for vehicular networks by proposing a new taxonomy. Finally, we present an analysis of the works included in this survey.

AHDNN: Attention-Enabled Hierarchical Deep Neural Network Framework for Enhancing Security of Connected and Autonomous Vehicles

The usage of the Internet of Things (IoT) in the field of transportation appears to have immense potential. Intelligent vehicle systems can exchange seamless information to assist cars to ensure better traffic control and road safety. The dynamic topology of this network, connecting a large number of vehicles, makes it vulnerable to several threats like authentication, data integrity, confidentiality, etc. These threats jeopardize the safety of vehicles, riders, and the entire system. Researchers are developing several approaches to combat security threats in connected and autonomous vehicles. Artificial Intelligence is being used by both scientists and hackers for protecting and attacking the networks, respectively. Nevertheless, wirelessly coupled cars on the network are in constant peril. This motivated us to develop an intrusion detection model that can be run in low-end devices with low processing and memory capacity and can prevent security threats and protect the connected vehicle network. This research paper presents an Attention-enabled Hierarchical Deep Neural Network (AHDNN) as a solution to detect intrusion and ensure autonomous vehicles’ security both at the nodes and at the network level. The proposed AHDNN framework has a very low false negative rate of 0.012 ensuring a very low rate of missing an intrusion in normal communication. This enables enhanced security in vehicular networks.

Physical layer security of vehicular networks with cooperative jamming helpers

As an important part of the intelligent transportation system (ITS), vehicular networks can provide drivers and passengers with more comfortable and convenient services such as efficient traffic management and infotainment. However, the security threats on data exchanges over vehicular networks have become increasingly severe. Different from conventional cryptographic technologies, the application of physical layer security (PLS) to vehicular networks has been investigated to prevent the security of exchanging data from the eavesdropper and measure precisely the leaked information to the eavesdropper, due to its low complexity and communication overhead. In this work, we are concerned with the PLS of cooperative vehicular networks consisting of a source vehicle, a destination vehicle, an eavesdropping vehicle and a cooperative jamming vehicle. First, to improve the secrecy performance, the cooperative jamming helper emits jamming signals to degrade the eavesdropping channel without harming the legitimate channel. Then, based on the Rayleigh fading channel models and the traffic models, the closed-form expressions of the secrecy outage probability (SOP) and the average secrecy capacity (ASC) of the considered vehicular networks are derived, which deliver more implications of various system parameters on SOP and ASC performances and can be computed without simulations at a lower complexity. Second, a definition of the optimal jamming vehicle is introduced and then the cooperative jamming vehicle selection strategy is presented. The existence of the optimal jamming vehicle is measured in probability, which is explored analytically. Third, the optimal power allocation that maximizes the secrecy capacity is found analytically for the source vehicle and the cooperative jamming helper. Finally, simulations are also presented to demonstrate the validation of these analytical results and confirm the advantages of the cooperative jamming strategy and the optimal power allocation. From the numerical results, more observations on the effects of the main system parameters on secrecy performances are obtained, which provides some useful guides for practice.

An Overview of Vehicular Cybersecurity for Intelligent Connected Vehicles

Cybersecurity is one of the most important challenges in the intelligent connected vehicle system. Interconnected vehicles are vulnerable to different network security attacks, which endanger the safety of passengers. This review paper firstly analyses the reasons why the current vehicle network is vulnerable to network attack and summarizes the three implementation methods of network security threats. The necessity of vehicle network security research and deployment is also analyzed. After giving a short introduction to the vehicular network security, this review paper identifies major security attacks on intelligent connected vehicles. Then the security enhancement technology of vehicle networks from three aspects are introduced, including vehicle network data encryption technology, vehicle network message authentication technology, and vehicle network anomaly intrusion detection technology. Then we analyze three common methods of abnormal intrusion detection in vehicle networks and explore the future research for preventing attacks on the network security of intelligent vehicle systems.

An Efficient and Secure Vehicular Networks Based on IoT and Cloud Computing

Undeniably, the increasing number of vehicles on the roads has automatically led to the unbelievable number of accidents occurring every day and the need for a safe communication system among the vehicles has become quite urgent. Using such systems, vehicles can communicate via wireless communication technologies and protocols like ZigBee, WiMax, Bluetooth, GSM, and RF. This paper introduces a communication system that works on preventing accidents by making all the surrounding vehicles communicate using RF, IoT, and LCD technologies to show all the warnings and the indications sent from other vehicles and a VLC that is connected to every vehicle to detect and send all needed information of occurring accidents on the road to all connected vehicles and to identify the hit or damaged ones. Moreover, the framework proposed in this paper attempts to perform mutual authentication procedures between vehicles and road-side units based on radio frequency identification devices and cloud computing. According to this framework, a low-cost device has been implemented using an LED lamp and an electrical circuit to control the LED luminous flux, and the modulated carrier signal has also been modified to effectively suppress the low-frequency noise. It should be noted that the experiments have shown that vehicular networks must be resilient and self-adapted to failures and potential threats to support coordinated transportation systems. Finally, the security analysis has also shown that the proposed framework is efficient, easily deployed and capable of finding the best routing paths between communicating vehicles.

Machine Learning for Security in Vehicular Networks: A Comprehensive Survey

Machine Learning (ML) has emerged as an attractive and viable technique to provide effective solutions for a wide range of application domains. An important application domain is vehicular networks wherein ML-based approaches are found to be very useful to address various problems. The use of wireless communication between vehicular nodes and/or infrastructure makes it vulnerable to different types of attacks. In this regard, ML and its variants are gaining popularity to detect attacks and deal with different kinds of security issues in vehicular communication. In this paper, we present a comprehensive survey of ML-based techniques for different security issues in vehicular networks. We first briefly introduce the basics of vehicular networks and different types of communications. Apart from the traditional vehicular networks, we also consider modern vehicular network architectures. We propose a taxonomy of security attacks in vehicular networks and discuss various security challenges and requirements. We classify the ML techniques developed in the literature according to their use in vehicular network applications. We explain the solution approaches and working principles of these ML techniques in addressing various security challenges and provide insightful discussion. The limitations and challenges in using ML-based methods in vehicular networks are discussed. Finally, we present observations and lessons learned before we conclude our work.

The Security Perspectives of Vehicular Networks: A Taxonomical Analysis of Attacks and Solutions

Vehicular networks are the combination of transport systems and the internet systems formed with the main motive to increase the safety of passengers, although non-safety applications are also provided by vehicular networks. Internet of Things (IoT) has a subsection called Mobile Ad hoc Network (MANET)m which in turn has a subsection called Vehicular Ad hoc Network (VANET). Internet of Energy (IoE) is a new domain that is formed using electric vehicles connected with VANETs. As a large number of transport systems are coming into operation and various pervasive applications are designed to handle such networks, the increasing number of attacks in this domain is also creating threats. As IoE is connected to VANETs extension with electric cars, the future of VANETs can be a question if security measures are not significant. The present survey is an attempt to cover various attack types on vehicular networks with existing security solutions available to handle these attacks. This study will help researchers in getting in-depth information about the taxonomy of vehicular network security issues which can be explored further to design innovative solutions. This knowledge will also be helpful for new research directions, which in turn will help in the formulation of new strategies to handle attacks in a much better way.

A Many-Objective Optimization Based Intelligent Intrusion Detection Algorithm for Enhancing Security of Vehicular Networks in 6G

With accelerated ensemble of the Internet of Things technology and automotive industry, vehicular network has been established as powerful tools. However, it is a significant challenge for dynamic and heterogeneous vehicular network to meet high requirements of the sixth-generation (6G) network such as high reliability and high security. To address this challenge, we design a novel weight-based ensemble machine learning algorithm (WBELA) to identify abnormal messages of vehicular Controller Area Network (CAN) bus network. Then, we establish a model based on many-objective optimization for intrusion detection of CAN bus network. To support this model, a many-objective optimization algorithm based on balance convergence and diversity (MaOEA-BCD) is designed. Open-source CAN bus message data sets and tamper attack scenarios are used to evaluate the effectiveness of proposed algorithm for different ID data frames. Experimental results revealed that proposed methods significantly enhance precision, reduce the false positive rate and have better performance than other methods so as to enhance security of vehicular networks in 6G.

Anomaly Detection in Vehicular CAN Bus Using Message Identifier Sequences

As the automotive industry moves forward, security of vehicular networks becomes increasingly important. Controller area network (CAN bus) remains as one of the most widely-used protocols for in-vehicle communication. In this work, we study an intrusion detection system (IDS) which detects anomalies in vehicular CAN bus traffic by analyzing message identifier sequences. We collected CAN bus data from a heavy-duty truck over a period of several months. First, we identify the properties of CAN bus traffic which enable the described approach, and demonstrate that they hold in different datasets collected from different vehicles. Then, we perform an experimental study of the IDS, using the collected CAN bus data and procedurally generated attacks. We analyze the performance of the IDS, considering various attack types and hyperparameter values. The analysis yields promising sensitivity and specificity values, as well as very fast decision times and acceptable memory footprint.

B-FERL: Blockchain based framework for securing smart vehicles

The ubiquity of connecting technologies in smart vehicles and the incremental automation of its functionalities promise significant benefits, including a significant decline in congestion and road fatalities. However, increasing automation and connectedness broadens the attack surface and heightens the likelihood of a malicious entity successfully executing an attack. In this paper, we propose a Blockchain based Framework for sEcuring smaRt vehicLes (B-FERL). B-FERL uses permissioned blockchain technology to tailor information access to restricted entities in the connected vehicle ecosystem. It also uses a challenge–response data exchange between the vehicles and roadside units to monitor the internal state of the vehicle to identify cases of in-vehicle network compromise. In order to enable authentic and valid communication in the vehicular network, only vehicles with a verifiable record in the blockchain can exchange messages. Through qualitative arguments, we show that B-FERL is resilient to identified attacks. Also, quantitative evaluations in an emulated scenario show that B-FERL ensures a suitable response time and required storage size compatible with realistic scenarios. Finally, we demonstrate how B-FERL achieves various important functions relevant to the automotive ecosystem such as trust management, vehicular forensics and secure vehicular networks.

Reliable and Efficient Content Sharing for 5G-Enabled Vehicular Networks

Conditional privacy preservation and message authentication serve as the primary research issues in terms of security in vehicular networks. With the arrival of 5G era, the downloading speed of network services and the message transmission speed have significantly improved. Consequently, the content exchanged by users in vehicular networks is not limited to traffic information, and vehicles moving at high speeds can share a wide variety of contents. However, sharing content reliably and efficiently remains challenging owing to the fast-moving character of vehicles. To solve this problem, we propose a reliable and efficient content sharing scheme in 5G-enabled vehicular networks. The vehicles with content downloading requests quickly filter the adjacent vehicles to choose capable and suitable proxy vehicles and request them for content services. Thus, the purpose of obtaining a good hit ratio, saving network traffic, reducing time delay, and easing congestion during peak hours can be achieved. The security analysis indicates that the proposed scheme meets the security requirements of vehicular networks. Our cryptographic operations are based on the elliptic curve, and finally, the proposed scheme also displays favorable performance compared to other related schemes.