Poisoning and Evasion Attacks Against Deep Learning Algorithms in Autonomous Vehicles

Jiang, Wenbo; Li, Hongwei; Liu, Sen; Luo, Xizhao; Lu, Rongxing

doi:10.1109/tvt.2020.2977378

Cited by 57 publications

(25 citation statements)

References 39 publications

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“…• AI/ML attack: Attack on AI algorithms or machine learning (ML) models can be triggered in several ways: 1) manipulations of traffic signs to deceive traffic sign recognition of CAVs [42], 2) data falsification, e.g., GPS locations [43], or 3) false driving maneuver signals to mislead models for misclassifying an input [44]. Also, a poisoning attack can reduce the prediction accuracy of the learned model by injecting malicious samples in the dataset that are used to train models [45], [46]. • Social engineering attack: The adversary manipulates users to make security mistakes or give away sensitive information that can be used for breaching the authentication or access control mechanisms.…”

Section: Software-related Attacksmentioning

confidence: 99%

An investigation of cyber-attacks and security mechanisms for connected and autonomous vehicles

Gupta¹,

Maple²

2023

Preprint

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<p>Edge Computing-based Connected Autonomous Vehicles (EC-CAVs) can fulfill the emerging demand for smart transportation on a global scale. Such innovations to transportation ecosystems can bring manyfold benefits from fully autonomous driving services to proactive vehicle monitoring and traffic management. Given the complexity involved in the deployment of EC-CAVs, zero-tolerance safety and security measures must be incorporated to avert vehicle immobilization, road accidents, disclosure of sensitive data, or any potential threats. This paper presents a reference architecture of EC-CAVs to investigate existing and emerging cyber-threats for deriving a common attack taxonomy for the EC-CAVs ecosystem. Subsequently, a comprehensive survey of security mechanisms is presented that can ensure safe and secure transportation of passengers from one destination to another using an EC-CAVs ecosystem.</p>

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Section: Software-related Attacksmentioning

confidence: 99%

An investigation of cyber-attacks and security mechanisms for connected and autonomous vehicles

Gupta¹,

Maple²

2023

Preprint

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“…The evasion and the poisoning threat in the AV were demonstrated by Jiang et al (2020). Here two various attacks were taken into consideration.…”

Section: Review Of Related Workmentioning

confidence: 99%

CNN‐OHGS: CNN‐oppositional‐based Henry gas solubility optimization model for autonomous vehicle control system

Ravikumar

Kavitha

2021

Journal of Field Robotics

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Numerous developments in technology toward autonomous vehicle systems (AVSs) have been performed for so many years all over the world. As our day-to-day life is becoming progressively dependent on automation vehicle system and control devices, the craze on automation advancements is expected to move closer through scientific technologies like artificial intelligence and robotics. From another point of view, the cyber threat to the AVS causes drastic accidents and traffic congestion by varying the speed differences among the vehicles. To overcome such shortcomings, this paper presented a convolutional neural network-oppositional-based Henry gas solubility optimization (CNN-OHGS) algorithm for an autonomous vehicle control system to enhance the robustness of the vehicle. At the same time, the attackers attempt to embed the faulty or defective data into the sensor readings of the autonomous vehicle to interrupt the optimal distances among the automated vehicles. Therefore to minimize such issues, our proposed framework employs the CNN-OHGS algorithm to reduce the distance variations among the vehicles thus ensuring the safety and optimal distance variation. Finally, the experimental analysis is conducted and the performance evaluation for various attacks, FID evaluation, and remorse function, and distance deviation for all sensor signal attacks are evaluated.The comparative analysis is made and we can clearly state that the proposed work has outperformed other existing approaches.

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“…Such attacks can be possibly initiated against deep learning models that require enthusiastically modernize their training data and learning parameters to cope with features of new attacks [62]. Second, the evasion attack in the basis of generating adversarial observations by adapting the attack structures to be somewhat dissimilar from the malevolent observations employed to train deep learning model; therefore, the possibility of detecting the attack get reduced, and even the attack evades the discovery, thereby dipping the performance of the system curiously [63]. Third, the impersonation attack, which struggles to simulate the original data observations to betray the deep learning models to categorize the original observations with various incorrect labels from the impersonated ones [64].…”

Section: Interdependent Interrelated and Collaborative Ecosystemsmentioning

confidence: 99%

Challenges, Opportunities, and Future Prospects

Abdel‐Basset

Moustafa

Hawash

et al. 2021

Deep Learning Techniques for IoT Security and Privacy

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Poisoning and Evasion Attacks Against Deep Learning Algorithms in Autonomous Vehicles

Cited by 57 publications

References 39 publications

An investigation of cyber-attacks and security mechanisms for connected and autonomous vehicles

An investigation of cyber-attacks and security mechanisms for connected and autonomous vehicles

CNN‐OHGS: CNN‐oppositional‐based Henry gas solubility optimization model for autonomous vehicle control system

Challenges, Opportunities, and Future Prospects

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