A Misbehavior Authority System for Sybil Attack Detection in C-ITS

Global misbehavior detection in Cooperative Intelligent Transport Systems (C–ITS) is carried out by a central entity named Misbehavior Authority (MA). The detection is based on local misbehavior detection information sent by Vehicle’s On–Board Units (OBUs) and by Road–Side Units (RSUs) called Misbehavior Reports (MBRs) to the MA. By analyzing these Misbehavior Reports (MBRs), the MA is able to compute various misbehavior detection information. In this work, we propose and evaluate different Machine Learning (ML)-based solutions for the internal detection process of the MA. We show through extensive simulation and several detection metrics the ability of solutions to precisely identify different misbehavior types.

In vehicular ad hoc networks (VANETs), vehicles communicate with each other and with roadside units (RSUs) in order to enhance road safety, improve traffic management and provide infotainment services. Along with the growth of VANETs, some challenges are emerging. Although there are many research work on VANETs, cheating attacks are still not well resolved such as selective message relaying attack, faked information reporting attack and resource-consuming attack launched by selfish or malicious participants. To deal with this kind of attacks, we present two novel lightweight security mechanisms by equipped each vehicle's On-Board Unit (OBU) with a small elegant module called TrInc, which is a trusted hardware and composed of only a non-decreasing counter and a key. We observe that TrInc-based method not only can effectively resist against cheating attacks in safety- oriented, convenience-oriented, and commercial-oriented VANET applications, but also significantly defend various aspects of security and privacy in VANETs. Compared with previous works, our proposal features low communication and computation overhead, less memory requirements, and good network scalability.

The intrusion detection systems are vital for the sustainability of Cooperative Intelligent Transportation Systems (C-ITS) and the detection of sybil attacks are particularly challenging. In this work, we propose a novel approach for the detection of sybil attacks in C-ITS environments. We provide an evaluation of our approach using extensive simulations that rely on real traces, showing our detection approach's effectiveness.

The United States in the early 20th century became the first nation to introduce the concept of intelligence in the transportation systems followed by intensive research and development by the European Countries like (Sweden, Switzerland), Japan, South Korea in the similar area. Since 1991, Intelligent Transport Systems (ITS) have been an emerging and promising area of research. This fact is also aided by the recent advancement and developments in the field of automated driving systems over the past few years. Cooperative Intelligent Transportation Systems (C-ITS) is the latest advancement over the existing Intelligent Transportation Systems (ITS) as now the systems are making use of communication and interaction with infrastructure using connected and automated vehicles (CAV’s) in order to create helpful and exciting applications that concern road safety, environment, mobility. Trust and Node Cooperation are the most crucial factors on which C-ITS relies as the decisions that are taken by a vehicle are based on the data and information it gets from the network infrastructure that is installed on the roadsides which is basically a pool of information which has been gathered from other vehicles and smart infrastructures that are part of the VANET. Presence of any misbehaviour in the network or a compromised node in the transport network system will lead to atrocious results for both traffic and network safety. Detecting any misbehaviour in the network can save us a lot of resources that will go into creating a system that can defend the complete network against a threat. Through the medium of our thesis project we will be detecting misbehaviour in the system using Deep Learning techniques. A most Basic MLP Neuron Architecture based Artificial Neural Network (ANN) would serve as our baseline and proposed models include LSTM and Transformer Model which will be used for time series classification.

Cooperative Intelligent Transport Systems (C-ITS) have seen increased interest in recent years, with several ongoing activities and first practical implementations. While clearly V2X-communication will play a significant role in all C-ITS deployments, several key components, such as the wireless communication technology and the message formats, are still evolving. This calls for a modular approach when designing C-ITS infrastructure, especially when considering a real world urban environment. Such environments are characterized by heterogeneous traffic light hardware and traffic management systems.This paper focuses on the modular architecture of the C-ITS pilot in Dresden. For example, a highly flexible communication stack is developed for this special purpose. It fully supports the message types standardized by European Telecommunications Standards Institute (ETSI) on the one hand, while on the other hand allowing the rapid implementation and test of novel message types and contents. This went hand in hand with the development of a modular roadside unit, enabling the rapid implementation of service applications on its central processing unit. In combination with a cloud-based backend, a real-time C-ITS service platform with a hybrid communication concept is developed.This paper presents a modular architecture and best practice guidelines based on experiences of the C-ITS pilot in Dresden. First results of research topics, such as latency measurements, are shown.The ongoing C-ITS deployment in Dresden is part of the initiative Synchrone Mobilitat 2023, which aims at advancing Intelligent Transport Systems. Focusing on automated and connected driving in urban areas, the research and development projects under its umbrella initiate scientific and technological developments and provide comprehensive test facilities. Automated and connected driving in urban areas requires not only simulations and test drives on test sites but also enormous testing effort under real traffic conditions. The Dresden Testbed offers outstanding conditions to conduct real-world test with a multitude of test kilometers and the experience of different traffic scenarios.

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.

Broadband Internet access provisioning in vehicular environments requires establishing on-the-fly connectivity between mobile vehicles and stationary Internet gateways deployed along roadways. The literature encloses various works revolving around vehicle-to-infrastructure (V2I) communication schemes designed to cater for this objective. In this paper, two novel complexity minimal MAC schemes are proposed for drive-thru Internet (DTI) access provisioning systems. The first of these schemes is called the random vehicle selection (RVS) scheme, and the second is called the least residual residence time (LRT) scheme. A mathematical framework is established with the objective of modeling a vehicle's onboard unit's buffer and evaluating its performance under RVS and LRT, in terms of several quality-of-service metrics. Extensive simulations are conducted for the purpose of verifying the proposed models' validity and accuracy.

A deep learning based misbehavior classification scheme for intrusion detection in cooperative intelligent transportation systems

Cooperative Intelligent Transportation Systems (CITS) are being developed to make road traffic safer and more efficient. Vehicle to vehicle (V2V) communication plays an important role for the cooperative awareness of vehicles in all the CITS standards in EU, US, and Japan. Essentially the CITS application relies on the exchanged data by V2V messages (called Cooperative Awareness Message (CAM) in EU). However, the V2V communication suffers from two significant problems: 1) mixed environment and 2) obstacle interference. First, the CITS application cannot be aware of a non-CAM-aware vehicle because of low market penetration ratio of the V2V message transmitter in the early deployment phase. Second, the V2V messages may not reach the receivers because of interference by the obstacle between the vehicles. To solve these problems, the proposed system called Proxy CAM generates the necessary V2V messages in the roadside unit on behalf of sender vehicles (or non-CAM-aware vehicle) by sensing the vehicle. We design the system based on the ITS Station architecture standardized in ISO/ETSI, working with any vehicle sensing technology. Moreover, we implement the prototype system with a stereo vision for the vehicle sensing. The prototype system is evaluated in an indoor test and also validated in a field test in Tokyo with some modifications in the link layer to adapt the Japanese regulation. The results show that Proxy CAM significantly improves the coverage of V2V messaging while the system overhead is limited.

Cooperative, connected and automated Intelligent Transportation System (ITS) aims to improve road traffic safety, comfort, security, and traffic management by sharing data/information among vehicles, infrastructure and road users. Cooperative ITS (C-ITS) also targets the reduction of environmental impact by the road transportation systems. Regarding self-driving vehicles, C-ITS plays a complementary role for enhancing on-board vehicle sensory data and thus making autonomous vehicles more robust and safe. In this paper we present the C-ITS platform, the test-cases and scenarios to be performed during the Lisbon Pilot of the AUTOCITS project. We also provide a description of the C-ITS Day-1 services, as simulated and also real-world events, that will be deployed and evaluated during the test-cases. Conventional, instrumented and autonomous vehicles will take part on the Lisbon Pilot, all equipped with on-board connected vehicular technologies. Two scenarios will be considered, motorway and urban-node scenarios, having distinct vehicles and test-cases, but all sharing the same C-ITS technology framework.

VANET is a next generation communication technology where vehicles create an autonomous network with assistance of RSU (Road Side Units). VANET provides legitimate information to the users on the road, in order to increase the road and user’s safety. It provides useful information to the vehicles about directions, location mapping, premises, etc. The fast propagation of emergency and local warning messages to the approaching vehicles will be helpful for preventing secondary accidents. If security of the network is not guaranteed, several attacks may occur, thereby alert messages may not reach to the RSUs on time or message may get spoof due to attack on it. Major attacks on emergency messages such as timing attack, spoofing attack, DoS attack, Sybil attack, etc. would be considered to mitigate and make the communication and infrastructure more secure. One of the major emergency message attack is Sybil, which is used by selfish message propagators to claim authenticity of their message by propagating with multiple identities. By deceiving other nodes with their false support using multiple identifies, they can achieve various purpose in network like vehicle re-routing, speed shift etc. This work proposes a RSU cooperative detection mechanism involving triangulation and fake propagation by RSU to identify the Sybil attacks in the VANET. The proposed methodology secures fast communication emergency messages to prevent secondary accidents by validating the message sources, nodes will accept only the messages from RSU which are digitally signed and source validated against Sybil nodes. The performance evaluation is made with existing methodology and effectiveness of the proposed methodology results demonstrated in article. The spatial location and trajectory of attacker is learnt effectively with RSU cooperation in the proposed work.

Cooperative Intelligent Transportation Systems (C-ITS) is one of the most prominent solutions to facilitate many new exciting applications concerning road safety, mobility, environment, and driving comfort. This technology is now on the verge of actual deployments. However, security threats, privacy, and trust management remain the most significant concerns. C-ITS relies highly on node cooperation and trust as vehicles take the decision based on the information received from the roadside network infrastructure. This information should be accurate and reliable to ensure proper functioning of the system. However, the presence of a misbehaving or compromised node in the system can lead to catastrophic results for both safety and traffic efficiency. It is therefore essential to detect misbehavior and defend the C-ITS against it. Although various studies have proposed misbehavior detection at the vehicular plane, the study that explores the machine learning capabilities to detect misbehavior at infrastructure plane is not present. Thus, in this paper, we propose a solution to detect misbehavior at the infrastructure plane of C-ITS that employs the predictive capabilities of Deep Learning. We compare the performance of Multi-Layer Perceptron (MLP) and Long Short-Term Memory (LSTM) models of Deep Neural Network.

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.

Cooperative intelligent transportation system (C-ITS) applications are generally susceptible to position spoofing-dependent attacks such as Sybil and DDoS attacks due to a lack of established solutions. This paper presents a novel cyber-physical blockchain cryptographic architecture to help prevent position spoofing attackers from becoming validated nodes in C-ITS applications. The solution also guarantees security requirements including the non-trivial non-repudiation in light of these and other attacks. With a use case of electronic toll collection (ETC), our architecture implements techniques based on Received Signal Strength Indication (RSSI) measurements in conjunction with blockchain authentication methods such as Proof-of-Location and smart contracts to determine the legitimacy of a node. We demonstrate our solution in experiments using ITS-G5 Cohda Wireless technology (a Road Side Unit and two On-Board Units programmed with the ITS Vanetza stack) with functionalities specified by the European Telecommunications Standardization Institute (ETSI). From our experimental results from several driving-based data gathering tests, we discovered that our solution is able to cope with noise and relative velocity challenges because it incorporates both OBUs and RSUs in the Proof of Location computation steps. In light of this, the proposed architecture may also be applicable to govern V2X in general.

In cooperative ITS, security and privacy protection are essential. Cooperative Awareness Message (CAM) is a basic V2V message standard, and misbehavior detection is critical for protection against attacking CAMs from the inside system, in addition to node authentication by Public Key Infrastructure (PKI). On the contrary, pseudonym IDs, which have been introduced to protect privacy from tracking, make it challenging to perform misbehavior detection. In this study, we improve the performance of misbehavior detection using observation data of other vehicles. This is referred to as collective perception message (CPM), which is becoming the new standard in European countries. We have experimented using realistic traffic scenarios and succeeded in reducing the rate of rejecting valid CAMs (false positive) by approximately 15 percentage points while maintaining the rate of correctly detecting attacks (true positive).