国際会議 · 2023

Hackers on the Go: Investigating Cyber Threats to Crowdsourced Mobility-on-Demand Platforms

Hamada Rizk , Hirozumi Yamaguchi

IEEE 33rd International Conference on Computer Theory and Applications (ICCTA), Keynote

出版社版 (DOI)

DOI: 10.1109/ICCTA60978.2023.10969231

Abstract

Mobility on Demand (MoD) has transformed transportation in smart cities, with accurate demand prediction being crucial for efficient MoD applications. Deep learning models have excelled in various domains and have been successfully applied to predict demand patterns in MoD. Leveraging diverse data sources, such as historical trip data and social media feeds, these models optimize resource allocation and service quality. However, the rise of adversarial attacks poses a threat to deep learning models. In this paper, we address the vulnerabilities in MoD systems relying on crowd-sourced data and propose a defense strategy to mitigate adversarial attacks. Our research aims to enhance the security and reliability of MoD systems, advancing the transportation industry.

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