Towards a Reliable Machine Learning-Based Global Misbehavior Detection in C–ITS: Model Evaluation Approach

Global misbehavior detection is an important backend mechanism in Cooperative Intelligent Transport Systems (C-ITS). It is based on the local misbehavior detection information sent by Vehicle's On-Board Units (OBUs) and by Road-Side Units (RSUs) called Misbehavior Reports (MBRs) to the Misbehavior Authority (MA). By analyzing these reports, the MA provides more accurate and robust misbehavior detection results. Sybil attacks pose a significant threat to the C-ITS systems. Their detection and identification may be inaccurate and confusing. In this work, we propose a Machine Learning (ML) based solution for the internal detection process of the MA. We show through extensive simulation that our solution is able to precisely identify the type of the Sybil attack and provide promising detection accuracy results.

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.

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.

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

Development and experiment of an intelligent connected cooperative vehicle infrastructure system based on multiple V2I modes and BWM-IGR method

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.

A Smartphone VANET Based Forward Collision Detection System

Finding a vacant place to park cars in the rush hour is time-consuming and even may be frustrating for the drivers. In some studies, vehicles are equipped with communicative tools called On Board Units (OBUs), which come along with roadside devices known as Roadside Units (RSUs), which allow the drivers to communicate each other and trace a vacant parking place easily. Previous systems work with connection of sensors all over the road and parking space and may result in spending much time to find a parking place, occupancy of the empty parking place until the car gets to the desired location, requirement for an additional hardware, wired network communication, and security issues. In this paper, we propose an exponential smoothing and multi-objective decision-making by using cloud-based methods to find the best parking place, taking into consideration of the park-cost for the driver. The proposed system uses the cellular base stations to eliminate the cost of the RSUs and the sensors. The results of our simulations via NS-2 network simulator confirm the efficiency of the proposed model.

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.

Vehicle-to-Vehicle (V2V) communications technology offers enhanced road safety, traffic efficiency, and connectivity. In V2V, vehicles cooperate by broadcasting safety messages to quickly detect and avoid dangerous situations on time or to avoid and reduce congestion. However, vehicles might misbehave, creating false information and sharing it with neighboring vehicles, such as, for example, failing to report an observed accident or falsely reporting one when none exists. If other vehicles detect such misbehavior, they can report it. However, false accusations also constitute misbehavior. In disconnected areas with limited infrastructure, the potential for misbehavior increases due to the scarcity of Roadside Units (RSUs) necessary for verifying the truthfulness of communications. In such a situation, identifying malicious behavior using a standard misbehaving management system is ineffective in areas with limited connectivity. This paper presents a novel mechanism, Distributed Reputation for Accurate Misbehavior Reporting (DRAMBR), offering a fully integrated reputation solution that utilizes reputation to enhance the accuracy of the reporting system by identifying misbehavior in rural networks. The system operates in two phases: offline, using the Local Misbehavior Detection Mechanism (LMDM), where vehicles detect misbehavior and store reports locally, and online, where these reports are sent to a central reputation server. DRAMBR aggregates the reports and integrates DBSCAN for clustering spatial and temporal misbehavior reports, Isolation Forest for anomaly detection, and Gaussian Mixture Models for probabilistic classification of reports. Additionally, Random Forest and XGBoost models are combined to improve decision accuracy. DRAMBR distinguishes between honest mistakes, intentional deception, and malicious reporting. Using an existing mechanism, the updated reputation is available even in an offline environment. Through simulations, we evaluate our proposed reputation system’s performance, demonstrating its effectiveness in achieving a reporting accuracy of approximately 98%. The findings highlight the potential of reputation-based strategies to minimize misbehavior and improve the reliability and security of V2V communications, particularly in rural areas with limited infrastructure, ultimately contributing to safer and more reliable transportation systems.

In urban vehicular network, existing routing protocols using beacon message scheme and P2P (peer to peer) density discovery scheme can hardly make a good performance as well as keep a high feasibility. This paper proposes an IGOR (improved geography and overlay routing) in urban vehicular environment. In IGOR, RSUs (road side units) are introduced to support the road density discovery. A passive neighbor discovery scheme is used by OBUs (on board units) instead of the beacon message scheme. The performance of IGOR is evaluated against the benchmark routing protocol of LOUVRE (landmark overlays for urban vehicular routing environment). Results show that IGOR has a higher accuracy in density discovery as well as a lower cost of the routing establishment and maintenance.

Along with the various technological advancements, the next generation vehicular networks such as the Internet of Vehicles (IoV) also bring in various cybersecurity challenges. To effectively address these challenges, in addition to the existing authentication techniques, there is also a need for identification of the misbehaving entities in the network. This letter proposes a deep learning-based classification framework to identify potential misbehaving vehicles before the communication requests from the On Board Units (OBUs) of the vehicles can be entertained by the network infrastructure such as the Road Side Units (RSUs). The evaluated metrics demonstrate the performance of the proposed classification approaches.

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.

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.

An intelligent transportation System (ITS) is an unconventional application that aims to provide traffic congestion information and emergency information to other vehicles. In ITS, the compilation and distribution of traffic event information by vehicles are of the most extreme significance. To establish an effective data-sharing network in ITS, the vehicles need to share the data in a protected form in real-time. Conventional ITS system includes data streaming from On-Board Unit (OBU) sensors through Road Side Units (RSU) to centralized cloud servers. Nevertheless, it is still a challenge to distribute essential event information in a selected area under the dynamic ITS environment and in the existence of a malicious vehicle. The current fundamental issues which emerge include privacy concerns due to third party management of cloud servers, single point of failure which are a bottleneck in data flow among the vehicles.