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

TL;DR

This paper introduces a GAN-based adversarial attack on behavioral models in mobile crowdsensing, demonstrating how malicious actors can manipulate worker selection and reduce victim workers' earnings while evading detection.

Contribution

It presents a novel GAN-based poisoning attack targeting behavioral models in mobile crowdsensing, highlighting vulnerabilities and potential impacts on worker selection and payment.

Findings

The attack effectively compromises worker behavior models.

The attack evades existing outlier detection methods.

Victim workers' payments are significantly reduced.

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.