Malicious users pose a significant security threat to mobile crowdsensing networks, severely impacting their service performance and data quality. However, existing binary (black-and-white) malicious user detection methods lack mechanisms for handling suspicious users, leaving persistent security vulnerabilities. To address this issue, this paper proposes a malicious user detection method  based on three-way decision. Firstly, an evaluation probability function is constructed using user behavior, data quality, and user recommendations as evaluation metrics. Then, the three-way decision method is employed to classify users into three categories: trust-worthy users, suspicious users, and malicious users. Finally, the grey correlation analysis method is utilized to dynamically identify malicious users among the suspicious ones. Simulation experiments demonstrate that the proposed detection method performs well in terms of accuracy, false positive rate, and false negative rate, effectively enhancing the security performance of mobile crowdsensing networks.