A new adversarial attack method based on Shapelet applied to traffic flow prediction model based on GCN

Mei Xu; Yan Kang; Hao Li

doi:10.1117/12.3013348

29 November 2023 A new adversarial attack method based on Shapelet applied to traffic flow prediction model based on GCN

Mei Xu, Yan Kang, Hao Li

Author Affiliations +

Proceedings Volume 12937, International Conference on Internet of Things and Machine Learning (IoTML 2023); 129370D (2023) https://doi.org/10.1117/12.3013348
Event: International Conference on Internet of Things and Machine Learning (IoTML 2023), 2023, Singapore, Singapore

Abstract

In this article, we propose a Shapelet based adversarial attack method for GCN based traffic flow prediction models. Firstly, we extract shape patterns from the original traffic flow data to obtain a set of shape feature Shapelets with significant discrimination. Then, we use Shapelet to generate adversarial samples using a method called reverse attack. Finally, we input adversarial samples into the GCN model to estimate their impact on traffic flow prediction. We evaluated the proposed attack algorithm on two real traffic datasets. Our algorithm can mislead model predictions, reduce the speed of model predictions, and lead to incorrect congestion prediction results. This study provides a new approach to adversarial attacks against time data. It also provides a new method for improving the robustness of traffic flow prediction models. Future research can further explore more complex and covert attack methods to address evolving security challenges.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Mei Xu, Yan Kang, and Hao Li "A new adversarial attack method based on Shapelet applied to traffic flow prediction model based on GCN", Proc. SPIE 12937, International Conference on Internet of Things and Machine Learning (IoTML 2023), 129370D (29 November 2023); https://doi.org/10.1117/12.3013348

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