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.

翻译：出行即服务（MaaS）整合了不同的交通模式，并可根据出行者的个人偏好、行为和意愿为其行程规划提供更高程度的个性化支持。为充分发挥MaaS的潜力，需要一系列人工智能（包括机器学习和数据挖掘）算法来学习个人需求、优化每位出行者及整体出行者的行程规划、协助交通服务运营商及相关政府机构运营和规划其服务，并检测和防范来自各类威胁行为者（包括不诚实或恶意的出行者与交通运营商）的网络攻击。在集中式和分布式场景中日益增多的各类人工智能与数据处理算法的使用，使得MaaS生态系统在AI算法层面和连接接口层面均面临多样化的网络与隐私攻击。本文首次系统综述了AI驱动的MaaS设计与多样化网络安全挑战（涉及网络攻击及应对措施）之间的耦合关系。特别聚焦于当前及新兴AI技术可能引发的隐私风险（用户画像、隐私推断及第三方威胁）和对抗性AI攻击（规避攻击、模型窃取及博弈操纵）对MaaS生态系统的影响。这些风险往往将新型攻击手段（如逆向学习）与传统攻击向量（如中间人攻击）相结合，加剧了广泛参与主体面临的风险，并对新兴商业模式构成威胁。