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

International audienceGlobal misbehavior detection in Cooperative Intelligent Transport Systems (C-ITS) is carried out by a central entity named Misbe-havior Authority (MA). The detection is based on local misbehavior detection information sent by Vehicle's On-Board Units (OBUs) and by RoadSide 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

A comprehensive survey of V2X cybersecurity mechanisms and future research paths

Recent advancements in vehicle-to-everything (V2X) communication have notably improved existing transport systems by enabling increased connectivity and driving autonomy levels. The remarkable benefits of V2X connectivity come inadvertently with challenges which involve security vulnerabilities and breaches. Addressing security concerns is essential for seamless and safe operation of mission-critical V2X use cases. This paper surveys current literature on V2X security and provides a systematic and comprehensive review of the most relevant security enhancements to date. An in-depth classification of V2X attacks is first performed according to key security and privacy requirements. Our methodology resumes with a taxonomy of security mechanisms based on their proactive/reactive defensive approach, which helps identify strengths and limitations of state-of-the-art countermeasures for V2X attacks. In addition, this paper delves into the potential of emerging security approaches leveraging artificial intelligence tools to meet security objectives. Promising data-driven solutions tailored to tackle security, privacy and trust issues are thoroughly discussed along with new threat vectors introduced inevitably by these enablers. The lessons learned from the detailed review of existing works are also compiled and highlighted. We conclude this survey with a structured synthesis of open challenges and future research directions to foster contributions in this prominent field.This work is supported by the H2020-INSPIRE-5Gplus project (under Grant agreement No. 871808), the ”Ministerio de Asuntos Económicos y Transformacion Digital” and the European Union-NextGenerationEU in the frameworks of the ”Plan de Recuperación, Transformación y Resiliencia” and of the ”Mecanismo de Recuperación y Resiliencia” under references TSI-063000-2021-39/40/41, and the CHIST-ERA-17-BDSI-003 FIREMAN project funded by the Spanish National Foundation (Grant PCI2019-103780).Peer ReviewedPostprint (published version

Self-reliant misbehavior detection in V2X networks

The safety and efficiency of vehicular communications rely on the correctness of the data exchanged between vehicles. Location spoofing is a proven and powerful attack against Vehicle-to-everything (V2X) communication systems that can cause traffic congestion and other safety hazards. Recent work also demonstrates practical spoofing attacks that can confuse intelligent transportation systems at road intersections. In this work, we propose two self-reliant schemes at the application layer and the physical layer to detect such misbehaviors. These schemes can be run independently by each vehicle and do not rely on the assumption that the majority of vehicles is honest. We first propose a scheme that uses application-layer plausibility checks as a feature vector for machine learning models. Our results show that this scheme improves the precision of the plausibility checks by over 20% by using them as feature vectors in KNN and SVM classifiers. We also show how to classify different types of known misbehaviors, once they are detected. We then propose three novel physical layer plausibility checks that leverage the received signal strength indicator (RSSI) of basic safety messages (BSMs). These plausibility checks have multi-step mechanisms to improve not only the detection rate, but also to decrease false positives. We comprehensively evaluate the performance of these plausibility checks using the VeReMi dataset (which we enhance along the way) for several types of attacks. We show that the best performing physical layer plausibility check among the three considered achieves an overall detection rate of 83.73% and a precision of 95.91%. The proposed application-layer and physical-layer plausibility checks provide a promising framework toward the deployment of on self-reliant misbehavior detection systems

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

International audienceGlobal misbehavior detection is an important back-end 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 RoadSide Units (RSUs) called Misbehavior Reports (MBRs) to the Mis-behavior 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

Identification of misbehavior detection solutions and risk scenarios in advanced connected and automated driving scenarios

The inclusion of 5G cellular communication system into vehicles, combined with other connected-vehicle technology, such as sensors and cameras, makes connected and advanced vehicles a promising application in the Cooperative Intelligent Transport Systems. One of the most challenging task is to provide resilience against misbehavior i.e., against vehicles that intentionally disseminate false information to deceive receivers and induce them to manoeuvre incorrectly or even dangerously. This calls for misbehaviour detection mechanisms, whose purpose is to analyze information semantics to detect and filter attacks. As a result, data correctness and integrity are ensured. Misbehaviour and its detection are rather new concepts in the literature; there is a lack of methods that leverage the available information to prove its trustworthiness. This is mainly because misbehaviour techniques come with several flavours and have different unpredictable purposes, therefore providing precise guidelines is rather ambitious. Moreover, dataset to test detection schemes are rare to find and inconvenient to customize and adapt according to needs. This work presents a misbehaviour detection scheme that exploits information shared between vehicles and received signal properties to investigate the behaviour of transmitters. Differently from most available solutions, this is based on the data of the on-board own resources of the vehicle. Computational effort and resources required are minor concerns, and concurrently time efficiency is gained. Also, the project addresses three different types of attack to show that detecting misbehaviour methods are more vulnerable to some profile of attacker than others. Moreover, a rich dataset was set up to test the scheme. The dataset was created according to the latest standardised evaluation methodologies and provides a valuable starting point for any further development and research

Speed Offset Attack Detection in Vehicular Ad-Hoc Networks (VANETs) Using Machine Learning

An integral component of the Intelligent Transportation System (ITS) is the emerging technology called Vehicular ad-hoc network (VANET). VANET allows Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communication wirelessly to improve road safety, traffic congestion, and information dissemination. Communication of vehicles in a VANET network is vulnerable to various attacks. Commonly used cryptographic techniques alone are insufficient to ensure and protect vehicle message integrity and authentication from insider attacks. In such cases, additional measures are necessary to ensure the correctness of the transmitted data. Each vehicle in the network periodically broadcasts a basic safety message (BSM) that contains essential status information about a vehicle, such as its position, speed, and heading to other vehicles and Road Side Units (RSU) to report its status. A speed offset attack is where an attacker (misbehaving vehicle) misleads the network by adding an offset value to its actual speed data in each BSM. Such attacks can result in traffic congestion and road accidents; therefore, it is essential to accurately detect and identify such attackers to ensure safety in the network. This research proposes a novel data-centric approach for detecting speed offset attacks using Machine Learning (ML) and Deep Learning (DL) algorithms. Vehicular Reference Misbehavior (VeReMi) Extension Dataset is used for this research. Preliminary results indicate that the proposed model can detect malicious nodes in the network quickly and accurately

Proof of Travel for Trust-Based Data Validation in V2I Communication Part I: Methodology

Previous work on misbehavior detection and trust management for Vehicle-to-Everything (V2X) communication can identify falsified and malicious messages, enabling witness vehicles to report observations about high-criticality traffic events. However, there may not exist enough "benign" vehicles with V2X connectivity or vehicle owners who are willing to opt-in in the early stages of connected-vehicle deployment. In this paper, we propose a security protocol for the communication between vehicles and infrastructure, titled Proof-of-Travel (POT), to answer the research question: How can we transform the power of cryptography techniques embedded within the protocol into social and economic mechanisms to simultaneously incentivize Vehicle-to-Infrastructure (V2I) data sharing activities and validate the data? The key idea is to determine the reputation of and the contribution made by a vehicle based on its distance traveled and the information it shared through V2I channels. In particular, the total vehicle miles traveled for a vehicle must be testified by digital signatures signed by each infrastructure component along the path of its movement. While building a chain of proofs of spatial movement creates burdens for malicious vehicles, acquiring proofs does not result in extra cost for normal vehicles, which naturally want to move from the origin to the destination. The proof of travel for a vehicle can then be used to determine the contribution and reward by its altruistic behaviors. We propose short-term and long-term incentive designs based on the POT protocol and evaluate their security and performance through theoretical analysis and simulations

A Survey on Machine Learning-based Misbehavior Detection Systems for 5G and Beyond Vehicular Networks

Advances in Vehicle-to-Everything (V2X) technology and onboard sensors have significantly accelerated deploying Connected and Automated Vehicles (CAVs). Integrating V2X with 5G has enabled Ultra-Reliable Low Latency Communications (URLLC) to CAVs. However, while communication performance has been enhanced, security and privacy issues have increased. Attacks have become more aggressive, and attackers have become more strategic. Public Key Infrastructure (PKI) proposed by standardization bodies cannot solely defend against these attacks. Thus, in complementary of that, sophisticated systems should be designed to detect such attacks and attackers. Machine Learning (ML) has recently emerged as a key enabler to secure future roads. Various V2X Misbehavior Detection Systems (MDSs) have adopted this paradigm. However, analyzing these systems is a research gap, and developing effective ML-based MDSs is still an open issue. To this end, this paper comprehensively surveys and classifies ML-based MDSs as well as discusses and analyses them from security and ML perspectives. It also provides some learned lessons and recommendations for guiding the development, validation, and deployment of ML-based MDSs. Finally, this paper highlighted open research and standardization issues with some future directions

Analyzing Attacks on Cooperative Adaptive Cruise Control (CACC)

Cooperative Adaptive Cruise Control (CACC) is one of the driving applications of vehicular ad-hoc networks (VANETs) and promises to bring more efficient and faster transportation through cooperative behavior between vehicles. In CACC, vehicles exchange information, which is relied on to partially automate driving; however, this reliance on cooperation requires resilience against attacks and other forms of misbehavior. In this paper, we propose a rigorous attacker model and an evaluation framework for this resilience by quantifying the attack impact, providing the necessary tools to compare controller resilience and attack effectiveness simultaneously. Although there are significant differences between the resilience of the three analyzed controllers, we show that each can be attacked effectively and easily through either jamming or data injection. Our results suggest a combination of misbehavior detection and resilient control algorithms with graceful degradation are necessary ingredients for secure and safe platoons.Comment: 8 pages (author version), 5 Figures, Accepted at 2017 IEEE Vehicular Networking Conference (VNC