Anomaly Detection Against GPS Spoofing Attacks on Connected and Autonomous Vehicles Using Learning From Demonstration

Yang, Zhen Hua; Jiang, Ying; Shen, Junjie; Feng, Yiheng; Chen, Qi Alfred; Mao, Z. Morley; Liu, Henry

doi:10.1109/tits.2023.3269029

Cited by 10 publications

(7 citation statements)

References 33 publications

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“…Our method was trained on 650 samples and achieved an average accuracy of 100%, which is 56% higher than deep learning methods [4] with the same amount of data. Based on the analysis of real‐world sensor attack models [2], we set the anomaly rate to 0.025%.GSA is design based on experiments [8]. We set the lane offset of the vehicle as a random value generated within the range

[ { - 3.5, - 1} ]\cup [ {1,3.5} ]

.…”

Section: Simulation and Discussionmentioning

confidence: 99%

Adaptive attack recognition method based on probability model for autonomous vehicle

Miao,

Shao,

et al. 2024

Electronics Letters

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The perception system is essential for autonomous vehicle safety and stability. However, on‐board sensors are vulnerable to external attacks, compromising driving strategies and security. Traditional attack detection methods, relying on static anomaly thresholds from data distributions, falter in complex driving scenarios. Recent studies have sought more nuanced detection techniques, including deep learning and inter‐vehicle communication, but these approaches face limitations related to data security and environmental dependency. This letter introduces an innovative, adaptive method for sensor attack detection in autonomous vehicles. By modelling the perception system's functions and employing the Gaussian process for probabilistic modelling, we generate dynamic uncertainty estimates that serve as adaptable anomaly boundaries. Our method's efficacy and resilience are validated through quantitative analysis of the real‐world KITTI dataset, demonstrating superior adaptive detection capabilities.

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[ { - 3.5, - 1} ]\cup [ {1,3.5} ]

.…”

Section: Simulation and Discussionmentioning

confidence: 99%

Adaptive attack recognition method based on probability model for autonomous vehicle

Miao,

Shao,

et al. 2024

Electronics Letters

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“…The data-centric approach utilizes data generated inside the vehicle to detect abnormalities for self-recovery or transmits this data to the outside to determine whether an abnormality exists in an external system [1][2][3][4]. Image-based approaches identify vehicles through image-processing technology and detect abnormal behavior based on this [5][6][7][8][9].…”

Section: Related Workmentioning

confidence: 99%

A Study on Machine Learning-Enhanced Roadside Unit-Based Detection of Abnormal Driving in Autonomous Vehicles

Yun,

Lee

et al. 2024

Electronics

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Ensuring the safety of autonomous vehicles is becoming increasingly important with ongoing technological advancements. In this paper, we suggest a machine learning-based approach for detecting and responding to various abnormal behaviors within the V2X system, a system that mirrors real-world road conditions. Our system, including the RSU, is designed to identify vehicles exhibiting abnormal driving. Abnormal driving can arise from various causes, such as communication delays, sensor errors, navigation system malfunctions, environmental challenges, and cybersecurity threats. We simulated exploring three primary scenarios of abnormal driving: sensor errors, overlapping vehicles, and counterflow driving. The applicability of machine learning algorithms for detecting these anomalies was evaluated. The Minisom algorithm, in particular, demonstrated high accuracy, recall, and precision in identifying sensor errors, vehicle overlaps, and counterflow situations. Notably, changes in the vehicle’s direction and its characteristics proved to be significant indicators in the Basic Safety Messages (BSM). We propose adding a new element called linePosition to BSM Part 2, enhancing our ability to promptly detect and address vehicle abnormalities. This addition underpins the technical capabilities of RSU systems equipped with edge computing, enabling real-time analysis of vehicle data and appropriate responsive measures. In this paper, we emphasize the effectiveness of machine learning in identifying and responding to the abnormal behavior of autonomous vehicles, offering new ways to enhance vehicle safety and facilitate smoother road traffic flow.

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“…The authors of [115] have addressed the security issues caused by GPS spoofing attacks facing the CV/AV localization system. In this work, after collecting a sufficient number of historical trajectories as a demonstration, maximum entropy inverse reinforcement learning will be adopted to derive the optimal driving policy that will be used to generate a predicted optimal trajectory.…”

Section: B Classical Machine Learning-based Techniquesmentioning

confidence: 99%

Anomaly Detection in Connected and Autonomous Vehicles: A Survey, Analysis, and Research Challenges

Baccari,

Hadded,

Ghazzai

et al. 2024

IEEE Access

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In Intelligent Transportation Systems (ITS), ensuring road safety has paved the way for innovative advancements such as autonomous driving. These self-driving vehicles, with their variety of sensors, harness the potential to minimize human driving errors and enhance transportation efficiency via sophisticated AI modules. However, the reliability of these sensors remains challenging, especially as they can be vulnerable to anomalies resulting from adverse weather, technical issues, and cyber-attacks. Such inconsistencies can lead to imprecise or erroneous navigation decisions for autonomous vehicles that can result in fatal consequences, e.g., failure in recognizing obstacles. This survey delivers a comprehensive review of the latest research on solutions for detecting anomalies in sensor data. After laying the foundation on the workings of the connected and autonomous vehicles, we categorize anomaly detection methods into three groups: statistical, classical machine learning, and deep learning techniques. We provide a qualitative assessment of these methods to underline existing research limitations. We conclude by spotlighting key research questions to enhance the dependability of autonomous driving in forthcoming studies.

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Anomaly Detection Against GPS Spoofing Attacks on Connected and Autonomous Vehicles Using Learning From Demonstration

Cited by 10 publications

References 33 publications

Adaptive attack recognition method based on probability model for autonomous vehicle

Adaptive attack recognition method based on probability model for autonomous vehicle

A Study on Machine Learning-Enhanced Roadside Unit-Based Detection of Abnormal Driving in Autonomous Vehicles

Anomaly Detection in Connected and Autonomous Vehicles: A Survey, Analysis, and Research Challenges

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