A Survey of AI-Related Cyber Security Risks and Countermeasures in Mobility-as-a-Service

TL;DR

This paper provides a comprehensive review of AI-driven cyber security risks and countermeasures in Mobility-as-a-Service, highlighting emerging threats and the need for robust defenses in this evolving ecosystem.

Contribution

It is the first detailed survey connecting AI-enabled MaaS systems with diverse cyber security challenges and potential countermeasures.

Findings

AI and data algorithms increase cyber attack surfaces in MaaS

Emerging AI privacy risks include profiling and inference attacks

Adversarial AI attacks such as evasion and extraction threaten MaaS security

Abstract

Mobility-as-a-Service (MaaS) integrates different transport modalities and can support more personalisation of travellers' journey planning based on their individual preferences, behaviours and wishes. To fully achieve the potential of MaaS, a range of AI (including machine learning and data mining) algorithms are needed to learn personal requirements and needs, to optimise journey planning of each traveller and all travellers as a whole, to help transport service operators and relevant governmental bodies to operate and plan their services, and to detect and prevent cyber attacks from various threat actors including dishonest and malicious travellers and transport operators. The increasing use of different AI and data processing algorithms in both centralised and distributed settings opens the MaaS ecosystem up to diverse cyber and privacy attacks at both the AI algorithm level and the connectivity surfaces. In this paper, we present the first comprehensive review on the coupling between AI-driven MaaS design and the diverse cyber security challenges related to cyber attacks and countermeasures. In particular, we focus on how current and emerging AI-facilitated privacy risks (profiling, inference, and third-party threats) and adversarial AI attacks (evasion, extraction, and gamification) may impact the MaaS ecosystem. These risks often combine novel attacks (e.g., inverse learning) with traditional attack vectors (e.g., man-in-the-middle attacks), exacerbating the risks for the wider participation actors and the emergence of new business models.