An Amalgamated Novel IDS Model for Misbehaviour Detection using VeReMiNet

Cooperative Intelligent Transport System (CITS) is a modern approach to safety and efficient traffic operation over Vehicle Ad-hoc Networks (VANETs). This approach is based on vehicles communication each with other or infrastructure communication through wireless connections. The security of VANETs has been identified as a vital component in C-ITS, because there may some attacks in such network that can expose the safety of the driver and passengers or other road users. Intrusion Detection Systems (IDS) is a crucial instrument that assists to defend the vehicular network by detecting the misbehaving vehicles. In this paper, a new ML technique that capitalizes on three new features basically related to the sender location, which in turn improves the performances in position transgression attacks is developed. In the final, the output of each considered K-Nearest Neighbor (KNN), Random Forest (RF) and Decision Tree (DT) is given to Stacking method such as Generalized Linear Model (GLM) in order to bring superiority in the detection performance. An IDS is constructed allowing vehicles to detect misbehavior in a distributed way, while the detection mechanism is trained centrally. The findings show that the proposed system outperforms the existing techniques using classification with an accuracy of $\mathbf{9 9. 8 2 \%}$, precision of $\mathbf{9 7. 3 4 \%}$, recall of $\mathbf{9 7. 5 6 \%}$, and F1-score of $\mathbf{9 7. 2 8 \%}$.

The traffic congestion frequently occurs in urban transportations. In order to alleviate the traffic congestion, the vehicle information and communication system (VICS) has been developed. However, since each vehicle can obtain global information on traffic congestion using VICS, all vehicles in the congested areas tend to move to non-congested areas. As a result, the non-congested areas become congested areas. To avoid such the oscillation between the congested and non-congested areas, this paper proposes a novel method based on the vehicle ad hoc network (VANET) for alleviating traffic congestion in urban transportations. In the proposed method, since each vehicle independently collects local information on traffic congestion using VANET, traffic can be distributed from congested areas to non-congested areas. Through simulation experiments, this paper shows that the proposed method provides faster velocity and shorter trip time than VICS in the environments that traffic varies temporally and spatially, which occur in urban transportations.

Vehicle Ad Hoc Network (VANET) is a type of wireless network consisting of vehicles and roadside communication devices. As VANET is associated with life-critical applications, it is important to consider each component of VANET design, functionality, software, and problems before introducing them. The important goal of VANET is to offer seamless communication for people traveling on the road to collect and relay message from every nearby vehicles in the event of urgent situations such as severe traffic jams, collisions, lane shift, speed limit, hazard or road condition alerts, position alert services, and in the event of climatic disasters, etc. Due to the high mobility of the vehicles, frequent network disconnection happens and many problems emerge in VANETs such as routing, synchronization, network congestion, network control, information management, privacy, security, etc. This chapter reviews the current state of the research on recent artificial intelligence techniques based VANET routing. The chapter also addresses on-going research works on the usage of artificial intelligence techniques for routing in VANET and future challenges need to be addressed in providing the artificial intelligence techniques for routing in VANETs.

The growing number of vehicles in cities has a great impact on our quality of life, such as air and noise pollution, traffic jams and traffic accidents. Cooperative Intelligent Transportation System (C-ITS) relies on communication technologies to provide innovative services and applications for transportation and traffic management. In the C-ITS context, users, roadside infrastructure and vehicles need to be connected and, for this purpose, a wide variety of wireless technologies can be used (e.g, vehicular WiFi, cellular and visible light communication). In this work we consider a VANET (Vehicular Ad-hoc NETwork) using vehicular WiFi (based on 802.11p). The communications in VANET networks have been studied for years and several routing algorithms have been developed for such a kind of network. However, a benchmark to compare the performance of such algorithms is still lacking. To fill this gap, the present work proposes a benchmark composed by instances of data routing for different scenarios in the VANET. Moreover, we propose a multi-objective algorithm based on ACO (Ant Colony Optimization) to compare with such benchmark. The results of simulations show the impact of several factors in the VANET connectivity, such as vehicle density, geographical location, propagation and fading models. The results are promising and indicate the importance of choosing appropriated simulation models.

In this demo, we introduce a Human-Interactive Hardware-In-the-Loop Simulation (HI2LS) framework for evaluating Cooperative Intelligent Transportation Systems (C-ITS) and road safety services. C-ITS is a complex system consisting of Wireless Access in Vehicular Environments (WAVE) communication devices, Human-Machine Interface (HMI), and computing elements to evaluate collision risk. Furthermore, C-ITS and services require the high reliability in a complicated operating environments including the human factors, and thus system testing is very important and essential process. In this demo, we propose a HI2LS framework for designing and evaluating road safety systems based on vehicle-to-vehicle/-infrastructure (V2X) communications. It enables developers to evaluate their road safety system designs in a wide range of virtual traffic environments and road conditions, which can replace dangerous and time-consuming field tests while guaranteeing the high reliability.

Emerging applications in Vehicle Ad hoc Networks (VANETs) not only open tremendous business opportunities and navigation benefits; they also pose formidable research challenges in security provisioning. A critical security threat to VANETs is false data injection, i.e., an attacker disseminates false information to disrupt the behavior of the other drivers. Information driven operations of vehicular networks make false data injection a very effective attack. As the first line of defense, this paper presents the notion of Proof-of-Relevance (PoR), which consists in proving that the event reporter is authentically relevant to the event it has reported. The PoR is accomplished by collecting authentic consensus on the event from witness vehicles in a cooperative way. Event reports from attackers who fail to provide this PoR are disregarded, making the network immune to bogus data. Performance evaluation and security analysis demonstrate the efficiency and security of the proposed scheme.

Security for cooperative intelligent transport systems (C-ITS), which are expected to contribute to realizing a safe and efficient transportation environment, will become more important in the near future. Among various proposed C-ITS services, this study examines security measures against cyber attacks for navigation services that collect sensor data from vehicles to estimate real-time traffic and distribute trip planning and route selection. In such services, a threat is assumed in which a malicious attacker prevents the traffic condition from being correctly recognized by injecting false data. As a result of such attacks, the services may fail to decide appropriate routes, and there is a concern that this cyber attack will likely disrupt traffic in the real world. By using a simulator on the basis of actual traffic data, we experimentally clarify that false data injection attacks against C-ITS services cause artificial congestion. Due to the potential for serious consequences of traffic disruption, security countermeasure technology is vital. To respond to cyber attacks that use false sensor data, technology to analyze sensor data and detect abnormalities is required. For this reason, we propose an analysis technique that efficiently analyzes sensor data transmitted from a huge number of vehicles to identify an attacker. The idea of the proposed method is to narrow down the sensor data for detailed analysis by screening on the basis of simple statistical analysis. The evaluation results show that the proposed method can efficiently detect false data injection attacks against C-ITS navigation services.

Improving the safety of transportation system is the main purpose of vehicle ad-hoc networks (VANETs). In order to improve traffic safety and ensure the event messages reliability, more and more researches have been focused on trust management to resolve the problems. However, in these researches, little attention has been paid to the location privacy due to the natural conflict between trust and anonymity, which is the basic protection of privacy. Although traffic safety remains the most crucial requirement in VANETs, location privacy can be just as important for drivers, and neither can be ignored. In this paper, we propose a self-organized trust management system that aims to thwart internal attackers from sending false messages in privacy-enhanced VANETS. To evaluate the reliability and performance of the proposed system, we conducted a set of simulations under collusion and non-collusion attacks. The simulation results show that the proposed system is highly resilient to adversarial attacks, no matter whether it is under a fixed silent period (FSP) or random silent period (RSP) location privacy-enhanced scheme.KeywordsVANETTrust ManagementSafetySelf-Organized

In the Vehicle Ad-hoc NETwork (VANET), moving vehicles organize into a mobile wireless Ad-hoc network to share online traffic information. Each vehicle can issue a declarative query for aggregating the traffic information from others in order to facilitate the navigation and avoid traffic jam. Existing query methods suffer from high latency, incomplete results, and large messages due to the movement of the vehicles in VANET. In this paper, we propose an adaptive query evaluation method based on the road network. In order to overcome the problems incurred by the movement, a relative static query evaluation plan is constructed based on the road network, and each vehicle can participate in the query evaluation plan autonomously. We also introduce control messages to notify the changed location of the query originator to other vehicles involved in the evaluation plan. In addition, we propose an one time message transferring based results collecting method to reduce the message cost. The optimization over the multiple queries is also discussed to reduce the messages further. We evaluate the performance of our method by extensive simulations. Experimental results show that our method can provide complete results within a short response time and small traffic overhead.

Vehicle Ad Hoc Networks(VANETs) can help reduce traffic accidents and improve road safety by broadcasting Emergency Messages(EMs) between vehicles in advance so that the vehicle can take action to avoid accidents. However, Its advantages are often compromised by factors such as high mobility, uneven nodes’ distribution, and signal attenuation, resulting in lower reliability and higher delay in the delivery of EMs. Besides, because of its open and mobility, VANETs are vulnerable to cyber security threats and are prone to multiple malicious attacks in the network. Malicious nodes can join the set of candidate forwarding nodes through collusion and identity forgery, which poses a serious challenge to EMs forwarding. In order to resolve the problems above, this paper proposes a geographic routing strategy to deliver EMs based on trusted nodes, focusing on measuring the reliability of link quality and node quality. The link quality between nodes is evaluated by measuring the actual transmission cost and the link signal-to-noise ratio to minimize the possible link interruption; at the same time, the node trust value is introduced to measure the credibility of the node and filter out the possible malicious nodes in the network. The research results show that the protocol is suitable for dense and sparse traffic conditions, can detect and identify malicious nodes, and has significant performance improvements in message delivery rate, end-to-end delay, and network throughput.

Today, more and more vehicles on the road are being connected via roadside communication networks for a wide range of services, ranging from road safety and navigation applications to in-vehicle entertainment. Cooperative Intelligent Transport Systems (C-ITS) services are aiming to improve road safety, traffic management and environmental performance of road transportation by sharing data between vehicles and road operator’s infrastructure via roadside communication networks. This paper investigates the capacity requirements of roadside ITS-G5 and cellular communication networks for supporting the emerging C-ITS applications, based on the C-ITS pilot implemented by Transport Infrastructure Ireland (TII). The results indicate that although the current communication networks are able to support the capacity demand of C-ITS applications at a low penetration rate of C-ITS equipped vehicles during the early uptake of C-ITS, new measures are required to improve the capacity of roadside communication networks when the number of C-ITS applications or the penetration rate of C-ITS equipped vehicles on the road goes high.

Performance evaluation of machine learning techniques in predicting cumulative absolute velocity

Vehicle ad-hoc network (VANET) is the main driving force to alleviate traffic congestion and accelerate the construction of intelligent transportation. However, the rapid growth of the number of vehicles makes the construction of the safety system of the vehicle network facing multiple tests. This paper proposes an identity-based aggregate signature scheme to protect the privacy of vehicle identity, receive messages in time and authenticate quickly in VANET. The scheme uses aggregate signature algorithm to aggregate the signatures of multiple users into one signature, and joins the idea of batch authentication to complete the authentication of multiple vehicular units, thereby improving the verification efficiency. In addition, the pseudoidentity of vehicles is used to achieve the purpose of vehicle anonymity and privacy protection. Finally, the secure storage of message signatures is effectively realized by using reliable cloud storage technology. Compared with similar schemes, this paper improves authentication efficiency while ensuring security, and has lower storage overhead.

Cooperative Intelligent Transport Systems (C-ITS) aim to enhance road safety, traffic efficiency and environmental performance of road transportation by enabling communication between the vehicles and the road infrastructure. Today, vehicle manufacturers and road operators are investing significantly in C-ITS technology and infrastructure implementation to realise its potential benefits. In Europe, the C-Roads platform aims to harmonise the deployment of C-ITS infrastructure and services across Europe. As part of the C-Roads platform, Transport Infrastructure Ireland implemented C-ITS pilot infrastructure to deliver C-ITS services to road users via ITS-G5 and cellular communications. For the deployment of ITS-G5 Roadside Units along a 60 km distance of the pilot routes in Ireland, a radio coverage planning exercise was undertaken to optimise the deployment. Several technical and practical factors were taken into consideration during this exercise. This paper outlines Transport Infrastructure Ireland’s C-ITS pilot infrastructure, discusses the key technical and practical factors taken into consideration and summarises the results achieved. As a result of this radio coverage planning exercise, we were able to achieve over 50% cost savings on infrastructure deployment for C-ITS services via ITS-G5 technology on the M50 and M1 motorways.

Vehicles have been connected for decades through over-the-air updates, infotainment systems, Wi–Fi, and Bluetooth interfaces. The emergence of cooperative intelligent transport systems (C-ITS) as the next generation of intelligent transport systems (ITS) has greatly expanded this connectivity. C-ITS is a key enabler for autonomous driving, providing real-time coordination between vehicles and infrastructure. By linking vehicles, infrastructure, road users, and cloud platforms through vehicle-to-everything (V2X) communication, C-ITS supports safer, more efficient, and intelligent transport. At the same time, expanded connectivity enlarges the cyberattack surface, exposing safety functions such as braking, steering, and traffic signal control to potential manipulation. This chapter contributes a literature-based conceptual mapping that uses a tri-level perspective to relate vulnerabilities to detection techniques. It pinpoints technical challenges, including false positives, latency, scalability, heterogeneity, and adversarial machine learning, and discusses strategies such as hybrid intrusion detection systems (IDS). To further enhance applicability, it presents methodologies including simulations, hardware-in-the-loop testbeds, and case studies based on C-Roads implementations. It further incorporates cybersecurity asset management and cybersecurity risk assessment as part of the C-ITS ecosystem, consistent with the NIST Cybersecurity Framework (CSF) and ISO/SAE 21434 standard, to enforce regulation and lifecycle-dependent security administration. The chapter also highlights the research challenges, which include asset-agnostic detection, insider and supply-chain threats, adversarial resilience, regulation alignment, and few real-world verifications, and provides a roadmap to further develop IDS research and practice toward C-ITS.