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1. Automated Discovery of Denial-of-Service Vulnerabilities in Connected Vehicle Protocols

   Hu, Shengtuo; Chen, Qi Alfred; Sun, Jiachen; Feng, Yiheng; Mao, Z. Morley; Liu, Henry X. (January 2021, USENIX Security Symposium)

   null (Ed.)

   With the development of the emerging Connected Vehicle (CV) technology, vehicles can wirelessly communicate with traffic infrastructure and other vehicles to exchange safety and mobility information in real time. However, the integrated communication capability inevitably increases the attack surface of vehicles, which can be exploited to cause safety hazard on the road. Thus, it is highly desirable to systematically understand design-level flaws in the current CV network stack as well as in CV applications, and the corresponding security/safety consequences so that these flaws can be proactively discovered and addressed before large-scale deployment. In this paper, we design CVAnalyzer, a system for discovering design-level flaws for availability violations of the CV network stack, as well as quantifying the corresponding security/safety consequences. To achieve this, CVAnalyzer combines the attack discovery capability of a general model checker and the quantitative threat assessment capability of a probabilistic model checker. Using CVAnalyzer, we successfully uncovered 4 new DoS (Denial-of-Service) vulnerabilities of the latest CV network protocols and 14 new DoS vulnerabilities of two CV platoon management protocols. Our quantification results show that these attacks can have as high as 99% success rates, and in the worst case can at least double the delay in packet processing, violating the latency requirement in CV communication.We implemented and validated all attacks in a real-world testbed, and also analyzed the fundamental causes to propose potential solutions. We have reported our findings in the CV network protocols to the IEEE 1609 Working Group, and the group has acknowledged the discovered vulnerabilities and plans to adopt our solutions. 

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2. A Grid-Based Misbehavior Detection System for Vehicular Communication Networks

   https://doi.org/10.1109/ICC51166.2024.10623059  

   Gunawardena, Chamath; Moushi, Owana Marzia; Ye, Feng; Hu, Rose Qingyang; Qian, Yi (June 2024, IEEE)

   A vehicular communication network allows vehicles on the road to be connected by wireless links, providing road safety in vehicular environments. Vehicular communication network is vulnerable to various types of attacks. Cryptographic techniques are used to prevent attacks such as message modification or vehicle impersonation. However, cryptographic techniques are not enough to protect against insider attacks where an attacking vehicle has already been authenticated in the network. Vehicular network safety services rely on periodic broadcasts of basic safety messages (BSMs) from vehicles in the network that contain important information about the vehicles such as position, speed, received signal strength (RSSI) etc. Malicious vehicles can inject false position information in a BSM to commit a position falsification attack which is one of the most dangerous insider attacks in vehicular networks. Position falsification attacks can lead to traffic jams or accidents given false position information from vehicles in the network. A misbehavior detection system (MDS) is an efficient way to detect such attacks and mitigate their impact. Existing MDSs require a large amount of features which increases the computational complexity to detect these attacks. In this paper, we propose a novel grid-based misbehavior detection system which utilizes the position information from the BSMs. Our model is tested on a publicly available dataset and is applied using five classification algorithms based on supervised learning. Our model performs multi-classification and is found to be superior compared to other existing methods that deal with position falsification attacks. 

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3. A Framework for Consistent and Repeatable Controller Area Network IDS Evaluation

   Agbaje, Paul; Anjum, Afia; Mitra, Arkajyoti; Bloom, Gedare; Olufowobi, Habeeb (April 2022, Fourth International Workshop on Automotive and Autonomous Vehicle Security)

   The landscape of automotive vehicle attack surfaces continues to grow, and vulnerabilities in the controller area network (CAN) expose vehicles to cyber-physical risks and attacks that can endanger the safety of passengers and pedestrians. Intrusion detection systems (IDS) for CAN have emerged as a key mitigation approach for these risks, but uniform methods to compare proposed IDS techniques are lacking. In this paper, we present a framework for comparative performance analysis of state-of-the-art IDSs for CAN bus to provide a consistent methodology to evaluate and assess proposed approaches. This framework relies on previously published datasets comprising message logs recorded from a real vehicle CAN bus coupled with traditional classifier performance metrics to reduce the discrepancies that arise when comparing IDS approaches from disparate sources. 

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4. Privacy Preserving Misbehavior Detection in IoV Using Federated Machine Learning

   https://doi.org/10.1109/CCNC49032.2021.9369513  

   Uprety, Aashma; Rawat, Danda B.; Li, Jiang (January 2021, 2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC))

   null (Ed.)

   Data falsification attack in Vehicular Ad hoc Networks (VANET) for the Internet of Vehicles (IoV) is achieved by corrupting the data exchanged between nodes with false information. Data is the most valuable asset these days from which many analyses and results can be drawn out. But the privacy concern raised by users has become the greatest hindrance in performing data analysis. In IoV, misbehavior detection can be performed by creating a machine learning model from basic safety message (BSM) dataset of vehicles. We propose a privacy-preserving misbehavior detecting system for IoV using Federated Machine Learning. Vehicles in VANET for IoV are given the initial dull model to locally train using their own local data. On doing this we get a collective smart model that can classify Position Falsification attack in VANET using the data generated by each vehicle. All this is done without actually sharing the data with any third party to perform analysis. In this paper, we compare the performance of the attack detection model trained by using a federated and central approach. This training method trains the model on a different kind of position falsification attack by using local BSM data generated on each vehicle. 

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5. On the Elliptic Curve Cryptography for Privacy-Aware Secure ACO-AODV Routing in Intent-Based Internet of Vehicles for Smart Cities

   https://doi.org/10.1109/TITS.2020.3008361  

   Safavat, Sunitha; Rawat, Danda B. (June 2020, IEEE Transactions on Intelligent Transportation Systems)

   null (Ed.)

   Internet of Vehicles (IoV) in 5G is regarded as a backbone for intelligent transportation system in smart city, where vehicles are expected to communicate with drivers, with road-side wireless infrastructure, with other vehicles, with traffic signals and different city infrastructure using vehicle-to-vehicle (V2V) and/or vehicle-to-infrastructure (V2I) communications. In IoV, the network topology changes based on drivers' destination, intent or vehicles' movements and road structure on which the vehicles travel. In IoV, vehicles are assumed to be equipped with computing devices to process data, storage devices to store data and communication devices to communicate with other vehicles or with roadside infrastructure (RSI). It is vital to authenticate data in IoV to make sure that legitimate data is being propagated in IoV. Thus, security stands as a vital factor in IoV. The existing literature contains some limitations for robust security in IoV such as high delay introduced by security algorithms, security without privacy, unreliable security and reduced overall communication efficiency. To address these issues, this paper proposes the Elliptic Curve Cryptography (ECC) based Ant Colony Optimization Ad hoc On-demand Distance Vector (ACO-AODV) routing protocol which avoids suspicious vehicles during message dissemination in IoV. Specifically, our proposed protocol comprises three components: i) certificate authority (CA) which maps vehicle's publicly available info such as number plates with cryptographic keys using ECC; ii) malicious vehicle (MV) detection algorithm which works based on trust level calculated using status message interactions; and iii) secure optimal path selection in an adaptive manner based on the intent of communications using ACO-AODV that avoids malicious vehicles. Experimental results illustrate that the proposed approach provides better results than the existing approaches. 

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