Decentralized Federated Learning (DFL), a paradigm for managing big data in a privacy-preserving and distributed manner, is vulnerable to poisoning attacks where malicious clients tamper with data or models. Current defense methods often assume Independently and Identically Distributed (IID) data across participants, which is unrealistic in real-world applications. In more realistic non-IID contexts, existing defensive strategies face challenges when distinguishing between models that have been compromised and those that have been trained on heterogeneous data distributions (non-IID), leading to diminished efficacy. In response, this paper proposes a framework that employs the Moving Target Defense (MTD) approach to bolster the robustness of DFL models. By continuously modifying the attack surface of the DFL system, the framework aims to mitigate poisoning attacks effectively. The proposed solution includes both proactive and reactive modes, utilizing a reputation system that combines metrics of model similarity and loss, alongside various defensive techniques. Comprehensive experimental evaluations indicate that the MTD-based mechanism significantly mitigates a range of poisoning attack types across multiple datasets with different federation topologies.