Truth discovery has emerged as a vital technique for resolving data conflicts in Mobile Crowdsensing. While prior studies have primarily focused on securing data transmission, they often overlook a critical threat: the distortion of inferred truths by malicious or unreliable users. In practice, even users deemed trustworthy may unintentionally provide inaccurate data due to environmental noise or coordinated manipulation by adversaries. To tackle these challenges, we propose a novel truth discovery algorithm that leverages the inherent volatility of sensing data to detect malicious users. Unlike traditional methods, our approach combines statistical inference with a dynamic reputation mechanism to identify and suppress malicious behaviors over time. Specifically, we first construct a baseline reputation scoring framework under the assumption that users are benign, using confidence intervals to quantify their reliability. We then integrate hypothesis testing and data volatility analysis to flag abnormal reporting patterns that may indicate adversarial behavior. Extensive simulations show that our method not only outperforms existing solutions in accurately identifying malicious users but also significantly enhances the overall quality of the aggregated truth.