Strategic Information Attacks on Incentive-Compatible Navigational Recommendations in Intelligent Transportation Systems

TL;DR

This paper investigates how cyber threats can manipulate navigational recommendation systems in intelligent transportation, proposing a game-theoretic approach to enhance their resilience against targeted information attacks.

Contribution

It introduces a coordinated incentive-compatible recommendation framework and analyzes vulnerabilities using a Stackelberg game model, highlighting susceptibility to misinformation attacks.

Findings

RS can be manipulated through fake user data

The Stackelberg model reveals attack vulnerabilities

Proposed methods improve system resilience

Abstract

Intelligent transportation systems (ITS) have gained significant attention from various communities, driven by rapid advancements in informational technology. Within the realm of ITS, navigational recommendation systems (RS) play a pivotal role, as users often face diverse path (route) options in such complex urban environments. However, RS is not immune to vulnerabilities, especially when confronted with potential information-based attacks. This study aims to explore the impacts of these cyber threats on RS, explicitly focusing on local targeted information attacks in which the attacker favors certain groups or businesses. We study human behaviors and propose the coordinated incentive-compatible RS that guides users toward a mixed Nash equilibrium, under which each user has no incentive to deviate from the recommendation. Then, we delve into the vulnerabilities within the recommendation process, focusing on scenarios involving misinformed demands. In such cases, the attacker can fabricate fake users to mislead the RS's recommendations. Using the Stackelberg game approach, the analytical results and the numerical case study reveal that RS is susceptible to informational attacks. This study highlights the need to consider informational attacks for a more resilient and effective navigational recommendation.