Browsing by Author "Chu, Kai-Fung"

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    A survey of artificial intelligence-related cybersecurity risks and countermeasures in Mobility-as-a-Service
    (Institute of Electrical and Electronics Engineers (IEEE), 2024-11) Chu, Kai-Fung; Yuan, Haiyue; Yuan, Jinsheng; Guo, Weisi; Balta-Ozkan, Nazmiye; Li, Shujun
    Mobility-as-a-service (MaaS) integrates different transport modalities and can support more personalization of travelers’ journey planning based on their individual preferences, behaviors and wishes. To fully achieve the potential of MaaS, a range of artificial intelligence (AI) (including machine learning and data mining) algorithms are needed to learn personal requirements and needs to optimize the journey planning of each traveler and all travelers as a whole, to help transport service operators and relevant governmental bodies to operate and plan their services, and to detect and prevent cyberattacks from various threat actors, including dishonest and malicious travelers and transport operators. The increasing use of different AI and data processing algorithms in both centralized 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 article, we present the first comprehensive review on the coupling between AI-driven MaaS design and the diverse cybersecurity challenges related to cyberattacks 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.
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    Deep reinforcement learning of passenger behavior in multimodal journey planning with proportional fairness
    (Springer, 2023-07-20) Chu, Kai-Fung; Guo, Weisi
    Multimodal transportation systems require an effective journey planner to allocate multiple passengers to transport operators. One example is mobility-as-a-service, a new mobility service that integrates various transport modes through a single platform. In such a multimodal and diverse journey planning problem, accommodating heterogeneous passengers with different and dynamic preferences can be challenging. Furthermore, passengers may behave based on experiences and expectations, in the sense that the transport experience affects their state and decision of the next transport service. Current methods of treating each journey planning optimization as a non-time varying single experience problem cannot adequately model passenger experience and memories over many journeys over time. In this paper, we model passenger experience as a Markov model where prior experiences have a transient effect on future long-term satisfaction and retention rate. As such, we formulate a multi-objective journey planning problem that considers individual passenger preferences, experiences, and memories. The proposed approach dynamically determines utility weights to obtain an optimal journey plan for individual passengers based on their status. To balance the profit received by each transport operator, we present a variant-based proportional fairness. Our experiments using real-world and synthetic datasets show that our approach enhances passenger satisfaction, compared to baseline methods. We demonstrate that the overall profit is increased by 2.3 times, resulting in a higher retention rate caused by higher satisfaction levels. Our proposed approach can facilitate the participation of transport operators and promote passenger acceptance of MaaS.
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    Federated reinforcement learning for consumers privacy protection in Mobility-as-a-Service
    (IEEE, 2024-02-13) Chu, Kai-Fung; Guo, Weisi
    Mobility-as-a-Service (MaaS) offers multi-modal transport modes in a single service platform, which requires tremendous data and software support. Among various types of data, consumers' data is vulnerable to the communication channel as it must be transmitted from the consumer end to the MaaS. Consumers put a high priority on the privacy of their data in selecting a service. This motivates the need for a secure information management system for MaaS to protect consumers' information from leakage. In this paper, we propose a federated reinforcement learning (FRL) approach for the information exchange intensive multi-modal journey planning process. The FRL approach protects the information from malicious information thieves by federating the global model training to a local one without sensitive information exchange while maintaining the same solution quality of enhancing MaaS profit and consumer satisfaction. We perform experiments on a test case based on New York City data. The results demonstrate that the FRL approach is effective in the MaaS multi-modal journey planning process. Compared to the baseline approaches, consumer satisfaction and MaaS profit increase by about 12% and 74%, respectively. This pilot study not only provides privacy protection insight into the MaaS multi-modal journey planning but also other privacy-concern applications.
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    Multi-agent reinforcement learning-based passenger spoofing attack on Mobility-as-a-Service
    (IEEE, 2024-11) Chu, Kai-Fung; Guo, Weisi
    Cyber-physical systems, such as smart transportation, face security threats from both digital and physical realms. Recently, Mobility-as-a-Service (MaaS) has emerged as a novel transportation concept, offering passengers access to diverse mobility services via a unified platform. Central to this system is the smart MaaS coordinator, tasked with tailoring services to passengers based on their profiles and behaviors. However, the coordination of heterogeneous passengers introduces vulnerabilities, enabling malicious entities to exploit the system by impersonating priority passengers with falsified information. Effective detection mechanisms require a deep understanding of the spoofing process. This paper investigates threats to the smart MaaS coordinator, unveiling a new reinforcement learning-based attack named the passenger spoofing attack, which aims to mitigate the risk of inadvertently exposing MaaS vulnerabilities post-deployment. This attack leverages feedback from actions and experiences to manipulate system profitability and passenger satisfaction by generating false passenger information. Furthermore, our research reveals that multi-agent reinforcement learning, accounting for spatial distribution among malicious agents and passengers, strengthens the attack. Through simulations based on datasets from New York City and synthetic sources, we demonstrate that the attack can significantly reduce 70% of profit and 50% of passenger satisfaction. Spatial analysis indicates an effective distance of approximately two nodes from the origin or destination. This study enriches our comprehension of the vulnerabilities inherent in smart coordinators within MaaS, enabling the development of robust countermeasures against malicious actors.
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    Passenger spoofing attack for artificial Intelligence-based Mobility-as-a-Service
    (IEEE, 2024-02-13) Chu, Kai-Fung; Guo, Weisi
    Mobility-as-a-Service (MaaS), a new mobility service model that integrates multiple mobility providers, relies on many data processing technologies to manage multi-modal transport. Artificial Intelligence (AI) is one of the technologies to improve the services matching to passengers based on their implicit experience and preference. However, incorporating AI into MaaS may also introduce loopholes to the system. One may use the loophole in the heterogeneity of passenger experience and preference by falsifying data to prioritize their journey, which jeopardizes the trustworthiness of MaaS. In this paper, we investigate the cyber security risks in MaaS, focusing on the spoofing attack in which malicious passengers are prioritized by falsifying data to gain an advantage in journey planning. The spoofing attack is based on reinforcement learning that learns to reduce passenger satisfaction about the MaaS and its profit by requesting travel with falsifying passenger states. We conduct experiments based on New York City dataset to evaluate the spoofing attack. The experiment results indicate that the attack can reduce about 70% of the profit. By investigating the cyber security risks in MaaS, we could enhance the knowledge and understanding of the risks for building a secure and trustworthy MaaS.
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    Privacy-preserving federated deep reinforcement learning for mobility-as-a-service
    (IEEE, 2023-10-03) Chu, Kai-Fung; Guo, Weisi
    Mobility-as-a-service (MaaS) is a new transport model that combines multiple transport modes in a single platform. Dynamic passenger behavior based on past experiences requires reinforcement-based optimization of MaaS services. Deep reinforcement learning (DRL) may improve passenger satisfaction by offering the most appropriate transport services based on individual passenger experiences and preferences. However, this produces a new privacy risk to the MaaS platform using the centralized DRL method. Information leakage will occur if the platform is not carefully designed with privacy-preserving mechanisms. In this paper, we propose a federated deep deterministic policy gradient (FDDPG) that maximizes passenger satisfaction and MaaS long-term profit while preserving privacy. We enforce an equally weighted experience sampling mechanism to prevent sampling bias such that the solution quality of FDDPG is statistically equivalent to the centralized algorithm. During the model training and inference, information is processed locally, and only the gradients are shared, which prevents information leakage to any semi-honest participants and eavesdroppers. Secure aggregation protocol in line with the dynamic property of the mobile agent is also used in the gradient sharing step to ensure that the algorithm is prevented from inference attacks. We perform experiments on New York City-based real-world and synthetic scenarios. The results show that the proposed FDDPG can improve the MaaS profit and passenger satisfaction by about 90% and 15%, respectively, and maintain stable training against agent dropout. Our approach and findings could enhance MaaS utility as well as facilitate passenger trust and participation in MaaS and other data-driven transportation systems.
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    Rewiring complex networks to achieve cluster synchronization using graph convolution networks with reinforcement learning
    (IEEE, 2024-06-10) Zou, Mengbang; Guo, Weisi; Chu, Kai-Fung
    Synchronization on complex networks depends on a myriad of factors such as embedded dynamics, initial conditions, network structure, etc. Current literature simplifies analysis of cluster synchronization leveraging conditions on network topology such as input-equivalence, network symmetries, etc., of which external equitable partition (EEP) is one of the most relaxed conditions. One practical problem is that for a dynamic system, how to alter a network to reach arbitrary achievable cluster synchronization and remaining faithful to the original structure. To solve this problem, we represent graph dynamics in Graph Convolution Network (GCN) modules that sit within an Actor-Critic Reinforcement Learning (AC-RL) framework under the condition of EEP. This allows the framework to select a good policy to sequentially rewire the network, where the sequence of moves matters. We test our method on two types of high-dimensional networked systems, Rossler dynamic networks and Hindmarsh-Rose neuronal circuits, with different network sizes. Our research opens up a way for the discovery of achievable cluster synchronization configurations by altering the network structure in any given networked dynamics.