Poisoning Behavioral-based Worker Selection in Mobile Crowdsensing using Generative Adversarial Networks

• URL: http://arxiv.org/abs/2506.05403v1
• Date: Wed, 04 Jun 2025 04:48:51 GMT
• Title: Poisoning Behavioral-based Worker Selection in Mobile Crowdsensing using Generative Adversarial Networks
• Authors: Ruba Nasser, Ahmed Alagha, Shakti Singh, Rabeb Mizouni, Hadi Otrok, Jamal Bentahar,
• Abstract summary: This work proposes an adversarial attack targeting behavioral-based selection models in Mobile Crowdsensing (MCS)<n>The proposed attack leverages Generative Adrial Networks (GANs) to generate poisoning points that can mislead the models during the training stage without being detected.
• Score: 14.727690033873657
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: With the widespread adoption of Artificial intelligence (AI), AI-based tools and components are becoming omnipresent in today's solutions. However, these components and tools are posing a significant threat when it comes to adversarial attacks. Mobile Crowdsensing (MCS) is a sensing paradigm that leverages the collective participation of workers and their smart devices to collect data. One of the key challenges faced at the selection stage is ensuring task completion due to workers' varying behavior. AI has been utilized to tackle this challenge by building unique models for each worker to predict their behavior. However, the integration of AI into the system introduces vulnerabilities that can be exploited by malicious insiders to reduce the revenue obtained by victim workers. This work proposes an adversarial attack targeting behavioral-based selection models in MCS. The proposed attack leverages Generative Adversarial Networks (GANs) to generate poisoning points that can mislead the models during the training stage without being detected. This way, the potential damage introduced by GANs on worker selection in MCS can be anticipated. Simulation results using a real-life dataset show the effectiveness of the proposed attack in compromising the victim workers' model and evading detection by an outlier detector, compared to a benchmark. In addition, the impact of the attack on reducing the payment obtained by victim workers is evaluated.

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