PRADA：面向导航路线推荐中误导性需求攻击的主动风险评估与缓解
PRADA: Proactive Risk Assessment and Mitigation of Misinformed Demand Attacks on Navigational Route Recommendations
AI总结：
本文提出PRADA博弈论框架，将攻击者与导航推荐系统的交互建模为Stackelberg博弈，评估误导性需求攻击风险，并引入信任机制缓解攻击影响，同时揭示了局部攻击可能改善整体交通的韧性悖论。
摘要：
利用无线通信、物联网和人工智能的最新进展，智能交通系统（ITS）在减少交通拥堵和提升用户体验方面发挥了重要作用。在ITS中，导航推荐系统（NRS）对于帮助用户简化城市环境中的路线选择至关重要。然而，NRS容易受到基于信息的攻击，这些攻击可以同时操纵NRS和用户，以实现恶意实体的目标。本研究旨在评估误导性需求攻击的风险，攻击者使用诸如基于Sybil的攻击等技术来操纵NRS所考虑的某些起点和终点的需求。我们提出了一个用于需求攻击主动风险评估的博弈论框架（PRADA），并将攻击者与NRS之间的交互建模为Stackelberg博弈。攻击者是领导者，传递误导性需求，而NRS是跟随者，对提供的信息做出响应。具体而言，我们考虑局部定向攻击的情形，攻击者的目标是使NRS将真实用户推荐到有利于某些群体的特定道路。我们的分析揭示了用户激励相容性与Wardrop均衡推荐之间的等价性，并表明当面对能够通过策略性伪造不存在的需求来显著改变用户路线的智能攻击者时，NRS及其用户处于高风险之中。为缓解这些风险，我们引入了一种信任机制，利用用户对NRS完整性的信心，并证明该机制能够有效降低误导性需求攻击的影响。数值实验用于验证上述结果，并展示了一种韧性悖论，即局部定向攻击有时反而有利于整体交通状况。
英文摘要：
Leveraging recent advances in wireless communication, IoT, and AI, intelligent transportation systems (ITS) played an important role in reducing traffic congestion and enhancing user experience. Within ITS, navigational recommendation systems (NRS) are essential for helping users simplify route choices in urban environments. However, NRS are vulnerable to information-based attacks that can manipulate both the NRS and users to achieve the objectives of the malicious entities. This study aims to assess the risks of misinformed demand attacks, where attackers use techniques like Sybil-based attacks to manipulate the demands of certain origins and destinations considered by the NRS. We propose a game-theoretic framework for proactive risk assessment of demand attacks (PRADA) and treat the interaction between attackers and the NRS as a Stackelberg game. The attacker is the leader who conveys misinformed demands, while the NRS is the follower responding to the provided information. Specifically, we consider the case of local-targeted attacks, in which the attacker aims to make the NRS recommend the authentic users towards a specific road that favors certain groups. Our analysis unveils the equivalence between users' incentive compatibility and Wardrop equilibrium recommendations and shows that the NRS and its users are at high risk when encountering intelligent attackers who can significantly alter user routes by strategically fabricating non-existent demands. To mitigate these risks, we introduce a trust mechanism that leverages users' confidence in the integrity of the NRS, and show that it can effectively reduce the impact of misinformed demand attacks. Numerical experiments are used to corroborate the results and demonstrate a Resilience Paradox, where locally targeted attacks can sometimes benefit the overall traffic conditions.
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